Hydrogen-doped comprehensive energy system multilayer dynamic energy control method considering time-space information integration

Abstract

The invention provides a hydrogen-doped comprehensive energy system multilayer dynamic energy control method considering time-space information integration. The method comprises the following steps: considering a comprehensive energy system operation framework comprising an energy layer, an information layer and a management layer, and establishing a hydrogen-doped gas turbine equipment model and a hydrogen-doped gas boiler equipment model; establishing a workload model and an energy consumption model of the data center, and analyzing energy consumption changes and corresponding regulation and control strategies of the data center at different time periods and in each region; establishing a data center demand response model and establishing constraint conditions; constructing a carbon transaction and carbon emission model; optimizing the dispatching model of the hydrogen-doped comprehensive energy system containing the data center by taking minimization of the economic cost and the carbon emission of the system as multiple targets; energy equipment output and data center work load distribution are corrected in real time, and multi-target optimization of economic cost and carbon emission is carried out on a hydrogen-doped comprehensive energy scheduling model of the integrated data center by optimizing and adjusting the fuel gas hydrogen-doped proportion and the operation state of a server.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage and scheduling. Specifically, it relates to a multi-layer dynamic energy control method for a hydrogen-blended integrated energy system that takes into account the integration of spatio-temporal information. Background Art

[0002] With the accelerating global energy transition, countries are taking low-carbon and efficient energy systems as the core strategy to address climate change. Traditional power systems rely on fossil fuels, leading to serious environmental pollution and greenhouse gas emissions. There is an urgent need to find greener and more sustainable energy solutions. Hydrogen energy has become the focus of global attention due to its zero-emission advantage. However, its production, storage, and transportation still face challenges of high cost and low efficiency. Especially when operating in coordination with traditional power systems, how to achieve efficient complementarity between the two remains a major problem. Based on this, we urgently need to integrate advanced energy storage technologies, load regulation strategies, and intelligent scheduling systems, and through real-time dynamic optimization, achieve deep coordination between hydrogen energy and power systems, thereby providing solid technical support and continuous impetus for the global energy transition.

[0003] As the energy demand of data centers continues to climb, their power consumption shows obvious spatio-temporal fluctuation characteristics. Especially during peak load periods, traditional power systems face great pressure. To address this challenge, spatio-temporal coordinated scheduling has become a key strategy for optimizing the energy management of data centers. By relying on high-speed optical communication networks and virtualization technologies, data centers can dynamically migrate computing tasks in the geographical space to ensure the balanced distribution of computing loads between different regions. For example, during periods of low power demand, data centers can postpone the processing of non-real-time tasks to achieve peak shaving and valley filling of the load. During periods of high electricity prices or tight power supply, data centers can migrate some workloads to regions with sufficient power supply to avoid local load overloading. The spatio-temporal coordinated scheduling mode enables data centers to flexibly respond in a dynamically changing power environment, reducing energy consumption while improving the stability of the power grid and the operating efficiency of data centers.

[0004] Based on the above-mentioned energy storage characteristics of hydrogen energy combined with the load scheduling requirements of power systems, and the advantages of spatio-temporal coordinated scheduling in optimizing the energy management of data centers, we urgently need to develop a new, multi-level, and dynamic integrated energy system scheduling and control method. This method can not only achieve real-time collaborative optimization between energy equipment and data centers, balance supply and demand, reduce energy consumption and power grid pressure, but also play an important role in reducing carbon emissions and achieving low-carbon economic goals, providing solid technical support for the global energy transition and the construction of smart cities. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art or related technologies.

[0006] For this reason, the purpose of the present invention is to propose a multi-layer dynamic energy control method for a hydrogen-blended integrated energy system that takes into account the integration of spatio-temporal information.

[0007] To achieve the above object, the technical solution of the present invention provides a multi-layer dynamic energy control method for a hydrogen-blended integrated energy system that takes into account the integration of spatio-temporal information. The control method includes: Step S1: Considering the operation architecture of an integrated energy system including an energy layer - information layer - management layer, introducing hydrogen blending into gas, and establishing a hydrogen-blended gas turbine equipment model and a hydrogen-blended gas boiler equipment model in the energy layer; Step S2: Establishing a data center workload model and a data center energy consumption model in the information layer, and analyzing the energy consumption changes and corresponding regulation strategies in different time periods and regions of the data center; wherein, the data center energy consumption model includes: a server operation energy consumption model and a cooling system energy consumption model; Step S3: Analyzing the characteristics of energy supply and load demand, establishing a data center demand response model in the management layer, and taking the number of servers powered on and the maximum delay time of the data center as constraint conditions; Step S4: Combining the deviation cost between the carbon emission quota and the actual carbon emissions, constructing a carbon trading and carbon emission model under the coordination of the management layer and the energy layer; Step S5: Incorporating the carbon trading cost into the objective function, taking the minimization of the system economic cost and carbon emissions as multiple objectives, and optimizing the scheduling model of the hydrogen-blended integrated energy system including the data center in the management layer to generate a multi-energy flow coordinated scheduling plan; Step S6: According to the generated multi-energy flow coordinated scheduling plan, real-time correcting the output of energy equipment and the distribution of data center workloads among the energy layer - information layer - management layer, and optimizing the dynamic control of the integrated energy system by optimizing and adjusting the hydrogen blending ratio of gas and the operation state of servers.

[0008] Preferably, in Step S1, the mathematical expression corresponding to the hydrogen-blended gas turbine equipment model is:

[0009]

[0010] In Equation (1), Y GT,t is the hydrogen blending ratio of the hydrogen-blended gas turbine; is the volume of hydrogen incorporated into the hydrogen-blended gas turbine; V gGT,t is the volume of natural gas input into the hydrogen-blended gas turbine; is the power of H2 incorporated into the hydrogen-blended gas turbine; is the calorific value of hydrogen; L gas is the calorific value of natural gas; P GTe,t is the electrical output power of the hydrogen-blended gas turbine; P GTh,t is the thermal output power of the hydrogen-blended gas turbine; P gGT,t is the gas power consumed by the hydrogen-blended gas turbine; η gteis the gas - electricity conversion efficiency of the hydrogen - doped gas turbine; η gth is the gas - heat conversion efficiency;

[0011] The mathematical expression corresponding to the hydrogen - doped gas boiler equipment model is:

[0012]

[0013] In formula (2), Y GB,t is the hydrogen doping ratio of the hydrogen - doped gas boiler; is the volume of hydrogen doped into the hydrogen - doped gas boiler; V gGB,t is the volume of natural gas input into the hydrogen - doped gas boiler; P gGB,t is the gas power consumed by the hydrogen - doped gas boiler; η gbh is the gas - heat conversion efficiency of the hydrogen - doped gas boiler; is the power of H2 doped into the hydrogen - doped gas boiler; P GBh,t is the thermal power output by the hydrogen - doped gas boiler.

[0014] Preferably, in step S2, the mathematical expression corresponding to the data center workload model is:

[0015]

[0016] In formula (3), λ i,t represents the actual workload of data center i in time period t; is the initial load assigned to data center i in time period t; is the amount of load transferred from the previous time period (t″ < t) to the current time period t; is the amount of load transferred from the current time period t to the subsequent time period (t′ > t); is the load transferred from other data center i″ to i; is the load transferred from data center i to i′;

[0017] The mathematical expressions corresponding to the server operation energy consumption model and the cooling system energy consumption model in the data center energy consumption model are:

[0018]

[0019] In formula (4), P IT (t) is the total power consumption of IT equipment, that is, the server operation energy consumption; N server is the number of servers; u(t) is the server utilization rate; P idle and P max are the idle power consumption and full - load power consumption of a single server respectively; P aux is the fixed power consumption of auxiliary equipment; P cool (t) is the cooling system energy consumption; COP(Tout (t) is the energy efficiency ratio of the cooling system; Q ext (t) is the heat input from the outside.

[0020] Preferably, step S3 specifically includes: Step S3.1: Establish the data center demand response model; where the mathematical expression corresponding to the data center demand response model is:

[0021]

[0022] In formula (5), is the effective response capacity of the data center; is the actual response capacity of the data center; is the reduced corresponding power during the response period; α1 and α2 are both effective response threshold coefficients;

[0023] Step S3.2: Establish a data center server constraint model to use the number of powered-on servers as a constraint condition; where the mathematical expression corresponding to the data center server constraint model is:

[0024]

[0025] In formula (6), s i,t represents the average server utilization rate of data center i at time t; S max is the maximum server utilization rate; M i is the total number of servers in data center i; m i,t represents the number of active servers in data center i at time t; μ is the workload that each server can handle per hour;

[0026] Step S3.3: Establish a data center maximum delay time constraint model to use the data center maximum delay time as a constraint condition; where the mathematical expression corresponding to the data center maximum delay time constraint model is:

[0027]

[0028] In formula (7), T i max is the maximum allowable delay time of data center i at time t.

[0029] Preferably, step S4 specifically includes: Step S4.1: Combine the deviation cost between the carbon emission quota and the actual carbon emissions to establish a carbon emission model for the hydrogen-blended unit; where the mathematical expression corresponding to the carbon emission model for the hydrogen-blended unit is:

[0030]

[0031] In Equation (9), P gGT,t , P gGB,t are the gas powers consumed by the hydrogen - blended gas turbine and the hydrogen - blended gas boiler respectively; E GT,t , E GB,t are the total carbon emissions of the hydrogen - blended gas turbine and the hydrogen - blended gas boiler respectively; E GTb,t , E GBb,t are the carbon emissions of the hydrogen - blended gas turbine and the hydrogen - blended gas boiler whose flue gases are diverted into the carbon capture equipment respectively; E GTp,t , E GBp,t are the carbon emissions of the hydrogen - blended gas turbine and the hydrogen - blended gas boiler whose flue gases are diverted and discharged into the atmosphere respectively; δ GT,t , δ GB,t are the flue gas diversion coefficients of the hydrogen - blended gas turbine and the hydrogen - blended gas boiler respectively; a1, b1, c1 are the carbon emission coefficients of the gas unit;

[0032] Step S4.2: Combine the deviation cost between the carbon emission quota and the actual carbon emissions to establish a data center carbon emission model; where the mathematical expression corresponding to the data center carbon emission model is:

[0033]

[0034] In Equation (10), E grid,t is the equivalent carbon emission of purchasing electricity from the power grid; P grid,t is the output power of the power grid; E sum,t , E t are the total carbon emissions and the actual carbon emissions respectively; E dc,t is the carbon quota value of the data center; a2, b2, c2 are the carbon emission calculation coefficients of the thermal power unit;

[0035] Step S4.3: Combine the deviation cost between the carbon emission quota and the actual carbon emissions to establish a data center carbon trading model; where the mathematical expression corresponding to the data center carbon trading model is:

[0036]

[0037] In Equation (11), σ is the carbon trading base price; δ is the compensation coefficient; θ is the carbon price growth coefficient; d is the length of the carbon trading interval.

[0038] Preferably, in Step S5, the mathematical expression corresponding to the step of incorporating the carbon trading cost into the objective function to minimize the system economic cost and carbon emissions as multiple objectives is:

[0039]

[0040] In Equation (12), F e is the cost on the energy supply side; F gridis the electricity purchase cost for the power grid; F gas is the gas purchase cost; F qf is the curtailment cost of wind power; F qg is the curtailment cost of solar power; F op is the operation cost; F el is the operation cost of the electrolyzer for hydrogen production; F mr is the operation cost of the methanation reactor; F fuel is the fuel cost of the carbon capture power plant; F cs is the carbon storage cost; F cbuy is the cost of purchasing CO2 externally; F DR is the settlement cost of demand response; β1 and β2 represent different weight coefficients;

[0041] In step S5, the scheduling model of the integrated hydrogen - blended energy system with a data center includes: the hydrogen - blended gas turbine equipment model, the hydrogen - blended gas boiler equipment model, the data center workload model, the data center energy consumption model, the data center demand response model, the data center server constraint model, the data center maximum latency time constraint model, the hydrogen - blended unit carbon emission model, the data center carbon emission model, the data center carbon trading model, and a mathematical expression with the minimization of system economic cost and carbon emissions as multiple objectives.

[0042] Advantages of the present invention:

[0043] The multi - layer dynamic energy control method of the integrated hydrogen - blended energy system considering spatio - temporal information integration provided by the present invention takes into account the integrated energy operation architecture including the energy layer - information layer - management layer, analyzes the impact of carbon emissions and introduces gas blending with hydrogen, and establishes models for hydrogen - blended gas turbines and hydrogen - blended gas boilers; aiming at the spatio - temporal adjustable characteristics of the energy consumption of the data center, establishes the data center workload and its energy consumption models; analyzes the characteristics of energy supply and load demand, establishes the carbon emission model and demand response model of the hydrogen - blended units in the data center, adds constraints such as the number of servers powered on and the maximum latency time to the operation control, and performs multi - objective optimization of economic cost and carbon emissions for the scheduling model of the integrated hydrogen - blended energy system with a data center.

[0044] The additional aspects and advantages of the present invention will become apparent in the following description or be learned through the practice of the present invention. Description of the Drawings

[0045] Figure 1 Shows a schematic flow chart of the multi - layer dynamic energy control method of the integrated hydrogen - blended energy system considering spatio - temporal information integration according to an embodiment of the present invention;

[0046] Figure 2 Shows a schematic flow chart of the optimal control of the integrated energy system with a data center according to an embodiment of the present invention;

[0047] Figure 3 Shows the architecture diagram of the multi - layer dynamic optimization control of the integrated energy system of an integrated data center according to an embodiment of the present invention;

[0048] Figure 4 Shows the simulation diagram of the workload scheduling of an integrated data center changing dynamically at different times of 24 hours a day according to an embodiment of the present invention;

[0049] Figure 5 Shows the simulation diagram of the number of powered - on servers in an integrated data center changing dynamically at different times of 24 hours a day according to an embodiment of the present invention. Detailed implementation manners

[0050] In order to be able to more clearly understand the above - mentioned objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0051] Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the limitations of the specific embodiments disclosed below.

[0052] Figure 1 Shows the schematic flowchart of the multi - layer dynamic energy control method of the hydrogen - blended integrated energy system considering the integration of spatio - temporal information. As Figure 1 shown, the multi - layer dynamic energy control method of the hydrogen - blended integrated energy system considering the integration of spatio - temporal information includes:

[0053] Step S1: Considering the operation architecture of the integrated energy system including the energy layer - information layer - management layer, introducing hydrogen blending into gas, and establishing a hydrogen - blended gas turbine equipment model and a hydrogen - blended gas boiler equipment model in the energy layer;

[0054] Step S2: Establishing a data center workload model and a data center energy consumption model in the information layer, and analyzing the energy consumption changes and their corresponding regulation strategies in different time periods and regions of the data center;

[0055] Step S3: Analyzing the characteristics of energy supply and load demand, establishing a data center demand response model in the management layer, and taking the number of powered - on servers and the maximum data center delay time as constraint conditions;

[0056] Step S4: Combining the deviation cost between the carbon emission quota and the actual carbon emissions, constructing a carbon trading and carbon emission model under the coordination of the management layer and the energy layer;

[0057] Step S5: Incorporate the carbon trading cost into the objective function, aiming to minimize both the system economic cost and carbon emissions. Optimize the dispatching model of the hydrogen-blended integrated energy system with a data center in the management layer to generate a multi-energy flow collaborative dispatching plan.

[0058] Step S6: According to the generated multi-energy flow collaborative dispatching plan, revise the energy device output and data center workload distribution in real time among the energy layer - information layer - management layer. Optimize the dynamic control of the integrated energy system by adjusting the hydrogen blending ratio of gas and the server operation status.

[0059] In this embodiment, the data center energy consumption model includes: the server operation energy consumption model and the cooling system energy consumption model.

[0060] In this embodiment, the multi-layer dynamic energy control method of the hydrogen-blended integrated energy system considering spatio-temporal information integration provided by the present invention takes into account the integrated energy operation architecture including the energy layer - information layer - management layer, analyzes the impact of carbon emissions and introduces hydrogen blending of gas, and establishes models of hydrogen-blended gas turbines and hydrogen-blended gas boilers; aiming at the spatio-temporal adjustable characteristics of the data center's energy consumption, establishes the data center workload and its energy consumption model; analyzes the characteristics of energy supply and load demand, establishes the carbon emission model and demand response model of the hydrogen-blended units in the data center, adds constraints such as the number of servers powered on and the maximum delay time to the operation control, and conducts multi-objective optimization of economic cost and carbon emissions for the hydrogen-blended integrated energy dispatching model integrating the data center.

[0061] In an embodiment of the present invention, in step S1, the mathematical expression corresponding to the hydrogen-blended gas turbine equipment model is:

[0062]

[0063] In formula (1), Y GT,t is the hydrogen blending ratio of the hydrogen-blended gas turbine; is the volume of hydrogen incorporated into the hydrogen-blended gas turbine; V gGT,t is the volume of natural gas input into the hydrogen-blended gas turbine; is the power of H2 incorporated into the hydrogen-blended gas turbine; is the calorific value of hydrogen; L gas is the calorific value of natural gas; P GTe,t is the electrical output power of the hydrogen-blended gas turbine; P GTh,t is the thermal output power of the hydrogen-blended gas turbine; P gGT,t is the gas power consumed by the hydrogen-blended gas turbine; η gte is the gas-electric conversion efficiency of the hydrogen-blended gas turbine; η gth is the gas-thermal conversion efficiency;

[0064] The mathematical expression corresponding to the hydrogen-blended gas boiler equipment model is:

[0065]

[0066] In formula (2), Y GB,t is the hydrogen blending ratio of the hydrogen-blended gas boiler; is the volume of hydrogen incorporated into the hydrogen-blended gas boiler; V gGB,t is the volume of natural gas input into the hydrogen-blended gas boiler; P gGB,t is the gas power consumed by the hydrogen-blended gas boiler; η gbh is the gas-to-heat conversion efficiency of the hydrogen-blended gas boiler; is the power of H2 incorporated into the hydrogen-blended gas boiler; P GBh,t is the thermal power output by the hydrogen-blended gas boiler.

[0067] In an embodiment of the present invention, in step S2, the mathematical expression corresponding to the data center workload model is:

[0068]

[0069] In formula (3), λ i,t represents the actual workload of data center i in time period t; is the initial load assigned to data center i in time period t; is the amount of load transferred from the previous time period (t″ < t) to the current time period t; is the amount of load transferred from the current time period t to the subsequent time period (t′ > t); is the load transferred from other data center i″ to i; is the load transferred from data center i to i′;

[0070] The mathematical expressions corresponding to the server operation energy consumption model and the cooling system energy consumption model in the data center energy consumption model are:

[0071]

[0072] In formula (4), P IT (t) is the total power consumption of IT equipment, i.e., the server operation energy consumption; N server is the number of servers; u(t) is the server utilization rate; P idle and P max are the idle power consumption and full-load power consumption of a single server respectively; P aux is the fixed power consumption of auxiliary equipment; P cool (t) is the cooling system energy consumption; COP(T out (t)) is the cooling system energy efficiency ratio; Q ext (t) is the external incoming heat.

[0073] In an embodiment of the present invention, step S3 specifically includes: Step S3.1: Establish a demand response model for the data center; wherein, the mathematical expression corresponding to the demand response model of the data center is:

[0074]

[0075] In Equation (5), is the effective response capacity of the data center; is the actual response capacity of the data center; is the reduced corresponding power during the response period; α1 and α2 are both effective response threshold coefficients;

[0076] Step S3.2: Establish a data center server constraint model to implement the number of powered-on servers as a constraint condition; wherein, the mathematical expression corresponding to the data center server constraint model is:

[0077]

[0078] In Equation (6), s i,t represents the average server utilization rate of data center i at time t; S max is the maximum server utilization rate; M i is the total number of servers in data center i; m i,t represents the number of active servers in data center i at time t; μ is the workload that each server can handle per hour;

[0079] Step S3.3: Establish a data center maximum delay time constraint model to implement the data center maximum delay time as a constraint condition; wherein, the mathematical expression corresponding to the data center maximum delay time constraint model is:

[0080]

[0081] In Equation (7), T i max is the maximum allowable delay time of data center i at time t.

[0082] In an embodiment of the present invention, step S4 specifically includes: Step S4.1: Establish a carbon emission model for the hydrogen-blended unit by combining the deviation cost between the carbon emission quota and the actual carbon emissions; wherein, the mathematical expression corresponding to the carbon emission model of the hydrogen-blended unit is:

[0083]

[0084] In Equation (8), P gGT,t 、P gGB,t are the gas powers consumed by the hydrogen-blended gas turbine and the hydrogen-blended gas boiler respectively; EGT,t , E GB,t are the total carbon emissions of the hydrogen - blended gas turbine and the hydrogen - blended gas boiler respectively; E GTb,t , E GBb,t are the carbon emissions of the hydrogen - blended gas turbine and the hydrogen - blended gas boiler when the flue gas is diverted into the carbon capture equipment respectively; E GTp,t , E GBp,t are the carbon emissions of the hydrogen - blended gas turbine and the hydrogen - blended gas boiler when the flue gas is diverted and discharged into the atmosphere respectively; δ GT,t , δ GB,t are the flue gas diversion coefficients of the hydrogen - blended gas turbine and the hydrogen - blended gas boiler respectively; a1, b1, c1 are the carbon emission coefficients of the gas unit;

[0085] Step S4.2: Combine the deviation cost between the carbon emission quota and the actual carbon emissions to establish a data center carbon emission model; where the mathematical expression corresponding to the data center carbon emission model is:

[0086]

[0087] In Equation (9), E grid,t is the equivalent carbon emission of power purchased from the power grid; P grid,t is the output power of the power grid; E sum,t , E t are the total carbon emissions and the actual carbon emissions respectively; E dc,t is the carbon quota value of the data center; a2, b2, c2 are the carbon emission calculation coefficients of the thermal power unit;

[0088] Step S4.3: Combine the deviation cost between the carbon emission quota and the actual carbon emissions to establish a data center carbon trading model; where the mathematical expression corresponding to the data center carbon trading model is:

[0089]

[0090] In Equation (11), σ is the base price of carbon trading; δ is the compensation coefficient; θ is the carbon price growth coefficient; d is the length of the carbon trading interval.

[0091] In an embodiment of the present invention, in step S5, the step of incorporating the carbon trading cost into the objective function to minimize the system economic cost and carbon emissions corresponds to the mathematical expression:

[0092]

[0093] In Equation (11), F e is the cost on the energy supply side; F grid is the cost of purchasing electricity from the power grid; F gas is the cost of purchasing gas; F qf is the cost of curtailed wind; F qgis the curtailment cost; F op is the operation cost; F el is the operation cost of the electrolyzer for hydrogen production; F mr is the operation cost of the methanation reactor; F fuel is the fuel cost of the carbon capture power plant; F cs is the carbon storage cost; F cbuy is the cost of purchasing CO2 externally; F DR is the settlement cost of demand response; β1 and β2 represent different weight coefficients;

[0094] In step S5, the scheduling model of the hydrogen - blended integrated energy system with a data center includes: the hydrogen - blended gas turbine equipment model, the hydrogen - blended gas boiler equipment model, the data center workload model, the data center energy consumption model, the data center demand response model, the data center server constraint model, the data center maximum latency time constraint model, the hydrogen - blended unit carbon emission model, the data center carbon emission model, the data center carbon trading model, and a mathematical expression with the minimization of the system economic cost and carbon emissions as multiple objectives.

[0095] Figure 2 shows a schematic flow chart of the optimal control of the integrated energy system of an integrated data center according to an embodiment of the present invention. As Figure 2 shown, the process of the optimal control of the integrated energy system of the integrated data center specifically includes the following steps: First, construct the IES dynamic balance equation based on the energy hub, analyze the fluctuations of the clean energy output and load demand of the IES, construct the models of the hydrogen - blended gas turbine and the hydrogen - blended gas boiler, and use the entropy weight method to initially configure the hydrogen blending ratio for a typical day, and then optimize and solve the optimal value of the hydrogen blending ratio of the gas turbine; Second, construct the data center workload and its energy consumption models, plan the operation plans of each data center, construct the data center demand response model and the operation cost and carbon emission models, and set the weight coefficients; Finally, constrain the number of servers, server utilization rate, and maximum latency time constraint, and perform scheduling and solution to see if it meets the scheduling requirements. If it meets, then issue the scheduling results of each unit in the IES with the data center, and complete the optimal scheduling.

[0096] Figure 2 The shown optimal control process reflects a complete route of "multi - energy flow balance modeling → data center load scheduling → carbon emission and carbon trading accounting → multi - objective scheduling and optimization". Based on this conclusion, we can further combine real - time load forecasting and load demands in various regions to formulate a more flexible coordination strategy between the energy layer and the information layer, so as to achieve more significant emission reduction benefits while improving the utilization rate of renewable energy.

[0097] Figure 3The architecture diagram of the multi - layer dynamic optimization control of the integrated data center's integrated energy system in an embodiment of the present invention is shown. Figure 3 The overall architecture highlights how to achieve the low - carbon operation and economic optimization of the system through multi - level collaboration in the scenario of "multi - energy flow + data center load regulation". The system not only needs to meet the real - time energy demand, but also dynamically affects the energy consumption characteristics and load distribution by adjusting the computing power of the data center, thereby further reducing the peak load pressure and carbon emissions.

[0098] Figure 4 The simulation diagram showing the dynamic change of the integrated data center workload scheduling at different times of a 24 - hour day in an embodiment of the present invention is shown. As Figure 4 shown, the yellow rectangle represents the workload amount migrated from data center 1 to data center 3, the purple rectangle represents the workload amount migrated from data center 1 to data center 2, the orange cells represent the total amount migrated out of data center 1, and the green rectangle represents the total amount migrated into data center 1. From Figure 4 (Data center workload scheduling simulation diagram), it can be seen that the scheduling model established by the present invention can flexibly allocate and migrate the data center workload in different time periods, thereby effectively balancing the energy consumption and workload in each period.

[0099] Figure 5 The simulation diagram showing the dynamic change of the number of powered - on servers in the integrated data center at different times of a 24 - hour day in an embodiment of the present invention is shown. As Figure 5 shown, the number of servers in the data center will be dynamically adjusted according to real - time load requirements and constraints such as maximum latency, so as to minimize energy consumption and carbon emissions while meeting the computing power requirements. Based on this conclusion, we can further optimize the task allocation and server on - off strategies in actual operation, use fewer computing resources to cope with the load peaks and valleys in different time periods, reduce the overall operating cost and reduce carbon emissions. The following will show a specific embodiment of the multi - layer dynamic energy control method of the integrated hydrogen - blended energy system considering spatio - temporal information integration of the present invention.

[0100] The implementation steps of the multi - layer dynamic energy control method of the integrated hydrogen - blended energy system considering spatio - temporal information integration in this specific embodiment are as follows:

[0101] 1) Step S0: Considering the energy production, conversion, and storage models of the energy supply equipment in the integrated energy system, establish an electricity - heat - gas - hydrogen conversion model and a multi - energy flow balance relationship.

[0102] Step S0 specifically includes:

[0103] Step S0.1: Establish a multi - energy flow balance relationship of electricity, heat, gas, and hydrogen for the energy supply equipment in the integrated energy system at time t; this multi - energy flow balance relationship is:

[0104]

[0105] In the above formula, P w,t is the output power of WT; P v,t is the output power of PV; P net,t is the net output power of the carbon capture power plant; P grid,t is the output power of the power grid; P EL,t is the operating power of the electrolytic hydrogen production; is the hydrogen power output by the electrolytic hydrogen production; P gas,t is the output power of the gas network; and are the injection powers of electricity storage, heat storage, gas storage, and hydrogen storage respectively; and are the release powers of electricity storage, heat storage, gas storage, and hydrogen storage respectively; P eload,t , P hload,t , P gload,t , and P hload,t are the electricity, heat, gas, and hydrogen loads respectively;

[0106] Step S0.2: Establish a photovoltaic generator output model; the mathematical expression corresponding to this photovoltaic generator output model is:

[0107]

[0108] In the above formula, P PV,t is the photovoltaic power; r and r max are the light intensity and the maximum intensity respectively; S is the module area; η is the photoelectric conversion efficiency;

[0109] Step S0.3: Establish a wind turbine output model; the mathematical expression corresponding to this wind turbine output model is:

[0110]

[0111] In the above formula, P WT,t is the wind power; v, v ci , v co , and v r are the wind speed of the wind turbine, the cut-in wind speed, the cut-out wind speed, and the rated wind speed respectively; P r is the rated power of the unit.

[0112] Step S0.4: Establish a battery energy storage model; the mathematical expression corresponding to this battery energy storage model is:

[0113]

[0114] In the above formula, R es,t represents the current capacity of the battery; are the maximum and minimum values of the charge and discharge powers respectively; R es,min and R es,max are the maximum and minimum values of the battery capacity respectively; are the binary identification variables for charging and discharging respectively. Since the battery cannot inject heat and release heat simultaneously, the two cannot both take the value of 1;

[0115] Step S0.5: Establish a mathematical model for the electrolyzer; the mathematical expression corresponding to this electrolyzer model is:

[0116]

[0117] In the above formula, η EL is the electrolyzer efficiency; m EL,t is the mass of hydrogen produced; HHV H2 is the calorific value of hydrogen; ρ H2 is the density of hydrogen (at room temperature and atmospheric pressure); P elmax is the maximum value of the electrolyzer operating power.

[0118] 2) Step S1: Consider the operation architecture of the integrated energy system including the energy layer - information layer - management layer, introduce hydrogen - blended gas, and establish a mathematical model for the hydrogen - blended gas turbine equipment and a mathematical model for the hydrogen - blended gas boiler equipment in the energy layer;

[0119] In Step S1, the mathematical expression corresponding to the hydrogen - blended gas turbine equipment model is:

[0120]

[0121] In Equation (1), Y GT,t is the hydrogen blending ratio of the hydrogen - blended gas turbine; is the volume of hydrogen incorporated into the hydrogen - blended gas turbine; V gGT,t is the volume of natural gas input into the hydrogen - blended gas turbine; is the power of H2 incorporated into the hydrogen - blended gas turbine; is the calorific value of hydrogen; L gas is the calorific value of natural gas; P GTe,t is the electrical output power of the hydrogen - blended gas turbine; P GTh,t is the thermal output power of the hydrogen - blended gas turbine; P gGT,t is the gas power consumed by the hydrogen - blended gas turbine; η gte is the gas - to - electricity conversion efficiency of the hydrogen - blended gas turbine; η gth is the gas - to - heat conversion efficiency;

[0122] The mathematical expression corresponding to the hydrogen - blended gas boiler equipment model is:

[0123]

[0124] In formula (2), Y GB,t is the hydrogen blending ratio of the hydrogen - blended gas boiler; is the volume of hydrogen incorporated into the hydrogen - blended gas boiler; V gGB,t is the volume of natural gas input into the hydrogen - blended gas boiler; P gGB,t is the gas power consumed by the hydrogen - blended gas boiler; η gbh is the gas - heat conversion efficiency of the hydrogen - blended gas boiler; is the power of H2 incorporated into the hydrogen - blended gas boiler; P GBh,t is the thermal power output by the hydrogen - blended gas boiler.

[0125] 3) Step S2: Establish a data center workload model and a data center energy consumption model in the information layer, and analyze the energy consumption changes and their corresponding regulation strategies in different time periods and regions of the data center; among them, the data center energy consumption model includes: a server operation energy consumption model and a cooling system energy consumption model;

[0126] In step S2, the mathematical expression corresponding to the data center workload model is:

[0127]

[0128] In formula (3), λ i,t represents the actual workload of data center i in time period t; is the initial load assigned to data center i in time period t; is the load transferred from the previous time period (t″ < t) to the current time period t; is the load transferred from the current time period t to the subsequent time period (t′ > t); is the load transferred from other data center i″ to i; is the load transferred from data center i to i′;

[0129] The mathematical expressions corresponding to the server operation energy consumption model and the cooling system energy consumption model in the data center energy consumption model are respectively:

[0130]

[0131] In formula (4), P IT (t) is the total power consumption of IT equipment, that is, the server operation energy consumption; N server is the number of servers; u(t) is the server utilization rate; P idle and P max are the idle power consumption and full - load power consumption of a single server respectively; P aux is the fixed power consumption of auxiliary equipment; P cool (t) is the cooling system energy consumption; COP(T out (t)) is the energy efficiency ratio of the cooling system; Qext (t) is the externally incoming heat.

[0132] 4) Step S3: Analyze the characteristics of energy supply and load demand, establish a data center demand response model in the management layer, and use the number of powered-on servers and the maximum data center latency time as constraint conditions to meet the real-time load demand;

[0133] The said step S3 specifically includes:

[0134] Step S3.1: Establish the data center demand response model; wherein, the mathematical expression corresponding to the data center demand response model is:

[0135]

[0136] In formula (5), is the effective response capacity of the data center; is the actual response capacity of the data center; is the reduced corresponding power during the response period; both α1 and α2 are effective response threshold coefficients;

[0137] Before performing step S3.2, establish a workload constraint model based on formula (3); the mathematical expression corresponding to this workload constraint model is:

[0138]

[0139] In the above formula, i ∈ I, t ∈ T; M i is the total number of servers in data center i; μ is the amount of workload that each server can handle per hour;

[0140] Step S3.2: Establish a data center server constraint model to implement using the number of powered-on servers as a constraint condition; wherein, the mathematical expression corresponding to the data center server constraint model is:

[0141]

[0142] In formula (6), s i,t represents the average server utilization rate of data center i at time t; S max is the maximum server utilization rate; M i is the total number of servers in data center i; m i,t represents the number of active servers in data center i at time t; μ is the amount of workload that each server can handle per hour;

[0143] Step S3.3: Establish a maximum delay time constraint model for the data center to implement the maximum delay time of the data center as a constraint condition; where the mathematical expression corresponding to the maximum delay time constraint model of the data center is:

[0144]

[0145] In Equation (7), T i max is the maximum allowable delay time of the data center at time period t of data center i;

[0146] Step S3.4: Establish an energy utilization constraint on the energy supply side; the mathematical expression corresponding to this energy utilization constraint on the energy supply side is:

[0147]

[0148] In the above formula, P wmax is the predicted output of wind power; P vmax is the predicted output of photovoltaic power; P gridmax,i , P gasmax,t are the limit values of electricity and gas purchases on a typical day, respectively;

[0149] Step S3.5: Establish the operating constraints of the gas turbine units;

[0150]

[0151] In the above formula, is the maximum value of the gas consumption power of GT; is the maximum value of the gas consumption power of GB; ΔP gGT , ΔP gGB are the power change limits of GT and GB, respectively.

[0152] 5) Step S4: Combine the deviation cost between the carbon emission quota and the actual carbon emissions, and construct a carbon trading and carbon emission model under the coordination of the management layer and the energy layer to reduce carbon emissions in the energy layer;

[0153] The specific steps of Step S4 include:

[0154] Step S4.1: Combine the deviation cost between the carbon emission quota and the actual carbon emissions, and establish a carbon emission model for the hydrogen-blended units; where the mathematical expression corresponding to the carbon emission model of the hydrogen-blended units is:

[0155]

[0156] In Equation (8), P gGT,t , P gGB,t are the gas powers consumed by the hydrogen-blended gas turbine and the hydrogen-blended gas boiler, respectively; E GT,t , E GB,tare the total carbon emissions of the hydrogen - blended gas turbine and the hydrogen - blended gas boiler, respectively; E GTb,t and E GBb,t are the carbon emissions of the hydrogen - blended gas turbine and the hydrogen - blended gas boiler when the flue gas is diverted into the carbon capture equipment, respectively; E GTp,t and E GBp,t are the carbon emissions of the hydrogen - blended gas turbine and the hydrogen - blended gas boiler when the flue gas is diverted and discharged into the atmosphere, respectively; δ GT,t and δ GB,t are the flue gas diversion coefficients of the hydrogen - blended gas turbine and the hydrogen - blended gas boiler, respectively; a1, b1, c1 are the carbon emission coefficients of the gas unit;

[0157] Step S4.2: Establish a carbon emission model for the data center by combining the deviation cost between the carbon emission quota and the actual carbon emissions; among them, the mathematical expression corresponding to the carbon emission model of the data center is:

[0158]

[0159] In Equation (9), E grid,t is the equivalent carbon emission of power purchase from the power grid; P grid,t is the output power of the power grid; E sum,t and E t are the total carbon emissions and the actual carbon emissions, respectively; E dc,t is the carbon quota value of the data center; a2, b2, c2 are the carbon emission calculation coefficients of the thermal power unit;

[0160] Step S4.3: Establish a carbon trading model for the data center by combining the deviation cost between the carbon emission quota and the actual carbon emissions; among them, the mathematical expression corresponding to the carbon trading model of the data center is:

[0161]

[0162] In Equation (10), σ is the carbon trading base price; δ is the compensation coefficient; θ is the carbon price growth coefficient; d is the length of the carbon trading interval, taking 200t.

[0163] 6) Step S5: Incorporate the carbon trading cost into the objective function, and optimize the scheduling model of the hydrogen - blended integrated energy system containing the data center in the management layer with the minimization of the system economic cost and carbon emissions as multiple objectives to generate a multi - energy flow collaborative scheduling plan;

[0164] In Step S5, the mathematical expression corresponding to the step of incorporating the carbon trading cost into the objective function and taking the minimization of the system economic cost and carbon emissions as multiple objectives is:

[0165]

[0166] In Equation (11), F eis the cost of the energy supply side; F grid is the cost of purchasing electricity from the power grid; F gas is the cost of purchasing gas; F qf is the cost of curtailed wind; F qg is the cost of curtailed solar; F op is the operating cost; F el is the operating cost of the electrolyzer for hydrogen production; F mr is the operating cost of the methanation reactor; F fuel is the fuel cost of the carbon capture power plant; F cs is the cost of carbon storage; F cbuy is the cost of purchasing CO2 externally; F DR is the settlement cost of demand response; β1 and β2 represent different weight coefficients; different values of β1 and β2 are set to adjust their proportions in the total objective function f, and the proportions satisfy the condition of β1 + β2 = 1;

[0167] In step S5, the dispatching model of the hydrogen - blended integrated energy system with a data center includes, but is not limited to: the hydrogen - blended gas turbine equipment model, the hydrogen - blended gas boiler equipment model, the data center workload model, the data center energy consumption model, the data center demand response model, the data center server constraint model, the data center maximum delay time constraint model, the hydrogen - blended unit carbon emission model, the data center carbon emission model, the data center carbon trading model, and a mathematical expression with the minimization of the system economic cost and carbon emissions as multiple objectives.

[0168] However, the entire dispatching model (i.e., the dispatching model of the hydrogen - blended integrated energy system with a data center) also includes each sub - model and constraint condition defined in the previous steps, jointly constituting the dispatching model of the hydrogen - blended integrated energy system with a data center.

[0169] 7) Step S6: According to the generated multi - energy flow collaborative dispatching plan, the output of energy equipment and the workload allocation of the data center are corrected in real - time between the energy layer - information layer - management layer. By optimizing and adjusting the gas - hydrogen blending ratio and the operating state of the server, the dynamic control of the integrated energy system is optimized.

[0170] In summary, the present invention provides a multi-layer dynamic energy control method for a hydrogen-doped integrated energy system that takes into account the integration of spatio-temporal information. Through the coordinated control of the energy layer, information layer, and management layer, this method realizes the dynamic optimal scheduling of the integrated energy system. The integrated energy system optimally solves the hydrogen-doped integrated energy system with a data center by correcting the output constraints of energy equipment and the load distribution of the data center. At the same time, it adjusts the hydrogen doping ratio in the gas and controls the number of powered-on servers, service rate, utilization rate, and regulation of no-load and peak power of the data center servers, so as to achieve the dynamic optimal control of the system economy and low-carbon emission goals on the premise of meeting the supply-demand balance and various operation constraints.

[0171] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A multi - layer dynamic energy control method for a hydrogen - blended integrated energy system considering spatio - temporal information integration, comprising: Step S1: Considering the operation architecture of an integrated energy system including an energy layer - information layer - management layer, introducing hydrogen blending in gas, and establishing a hydrogen - blended gas turbine equipment model and a hydrogen - blended gas boiler equipment model in the energy layer; Step S2: Establishing a data center workload model and a data center energy consumption model in the information layer, and analyzing the energy consumption changes and corresponding regulation strategies in different time periods and regions of the data center; wherein, the data center energy consumption model includes: a server operation energy consumption model and a cooling system energy consumption model; Step S3: Analyzing the characteristics of energy supply and load demand, establishing a data center demand response model in the management layer, and taking the number of server startups and the maximum data center delay time as constraint conditions; Step S4: Combining the deviation cost between the carbon emission quota and the actual carbon emissions, constructing a carbon trading and carbon emission model under the coordination of the management layer and the energy layer; Step S5: Incorporating the carbon trading cost into the objective function, taking the minimization of the system economic cost and carbon emissions as multi - objectives, optimizing the scheduling model of the hydrogen - blended integrated energy system with a data center in the management layer to generate a multi - energy flow collaborative scheduling plan; Step S6: According to the generated multi - energy flow collaborative scheduling plan, real - time correcting the energy device output and data center workload allocation between the energy layer - information layer - management layer, and optimizing the dynamic control of the integrated energy system by optimizing and adjusting the hydrogen blending ratio of gas and the server operation state.

2. The multi-layer dynamic energy control method for a hydrogen-doped integrated energy system considering spatio-temporal information integration according to claim 1, characterized in that In step S1, the mathematical expression corresponding to the hydrogen - blended gas turbine equipment model is: In formula (1), Y GT,t is the hydrogen blending ratio of the hydrogen-blended gas turbine; is the volume of hydrogen incorporated into the hydrogen-blended gas turbine; V gGT,t is the volume of natural gas input into the hydrogen-blended gas turbine; is the power of H2 incorporated into the hydrogen-blended gas turbine; HHV H2 is the calorific value of hydrogen; L gas is the calorific value of natural gas; P GTe,t is the electrical output power of the hydrogen-blended gas turbine; P GT h,t is the thermal output power of the hydrogen-blended gas turbine; P gGT,t is the gas power consumed by the hydrogen-blended gas turbine; ηgte is the gas-electric conversion efficiency of the hydrogen-blended gas turbine; ηgth is the gas-thermal conversion efficiency; The mathematical expression corresponding to the hydrogen - blended gas boiler equipment model is: In formula (2), Y GB , where t is the hydrogen blending ratio of the hydrogen-blended gas boiler; is the volume of hydrogen incorporated into the hydrogen-blended gas boiler; Vg GB , where t is the volume of natural gas input into the hydrogen-blended gas boiler; Pg GB , where t is the gas power consumed by the hydrogen-blended gas boiler; η gbh is the gas-thermal conversion efficiency of the hydrogen-doped gas boiler; is the power of H2 doped in the hydrogen-doped gas boiler; P GB h,t is the thermal power output by the hydrogen-doped gas boiler.

3. The multi-layer dynamic energy control method for a hydrogen-doped integrated energy system considering spatio-temporal information integration according to claim 2, characterized in that, In step S2, the mathematical expression corresponding to the data center workload model is: In formula (3), λ i,t represents the actual workload of data center i in time period t; is the initial load allocated to data center i in time period t; is the amount of load transferred from the previous time period (t″ < t) to the current time period t; is the amount of load transferred from the current time period t to the subsequent time period (t′ > t); is the load transferred from other data center i″ to i; is the load transferred from data center i to i′; The mathematical expressions corresponding to the server operation energy consumption model and the cooling system energy consumption model in the data center energy consumption model are respectively: In formula (4), P IT (t) is the total power consumption of IT equipment, that is, the energy consumption of server operation; N server is the number of servers; u(t) is the server utilization rate; P idle and P max are the idle power consumption and full-load power consumption of a single server respectively; P aux is the fixed power consumption of auxiliary equipment; P cool (t) is the energy consumption of the cooling system; COP(T out (t)) is the energy efficiency ratio of the cooling system; Q ext (t) is the external incoming heat.

4. The multi-layer dynamic energy control method for a hydrogen-doped integrated energy system considering spatio-temporal information integration according to claim 3, characterized in that Step S3 specifically includes: Step S3.1: Establishing the data center demand response model; wherein, the mathematical expression corresponding to the data center demand response model is: In formula (5), is the effective response capacity of the data center; is the actual response capacity of the data center; is the reduced corresponding power during the response period; both α1 and α2 are effective response threshold coefficients; Step S3.2: Establishing a data center server constraint model to implement taking the number of server startups as a constraint condition; wherein, the mathematical expression corresponding to the data center server constraint model is: In formula (6), s i,t represents the average server utilization rate of data center i during period t; S max is the maximum server utilization rate; M i is the total number of servers in data center i; m i,t represents the number of active servers in data center i during period t; μ is the amount of workload that each server can handle per hour; Step S3.3: Establishing a data center maximum delay time constraint model to implement taking the data center maximum delay time as a constraint condition; wherein, the mathematical expression corresponding to the data center maximum delay time constraint model is: In formula (7), is the maximum allowable delay time of data center i at time period t.

5. The multi-layer dynamic energy control method for a hydrogen-doped integrated energy system considering spatio-temporal information integration according to claim 4, characterized in that Step S4 specifically includes: Step S4.1: Combining the deviation cost between the carbon emission quota and the actual carbon emissions, establishing a hydrogen - blended unit carbon emission model; wherein, the mathematical expression corresponding to the hydrogen - blended unit carbon emission model is: In Equation (8), P gGT,t and P gGB,t are the gas powers consumed by the hydrogen-doped gas turbine and the hydrogen-doped gas boiler, respectively; E GT,t and E GB,t are the total carbon emissions of the hydrogen-doped gas turbine and the hydrogen-doped gas boiler, respectively; E GTb,t and E GBb,t are the carbon emissions of the hydrogen-doped gas turbine and the hydrogen-doped gas boiler when the flue gas is diverted into the carbon capture equipment, respectively; E GTp,t and E GBp,t are the carbon emissions of the hydrogen-doped gas turbine and the hydrogen-doped gas boiler when the flue gas is diverted and discharged into the atmosphere, respectively; δ GT,t and δ GB,t are the flue gas diversion coefficients of the hydrogen-doped gas turbine and the hydrogen-doped gas boiler, respectively; a1, b1, and c1 are the carbon emission coefficients of the gas unit; Step S4.2: Combining the deviation cost between the carbon emission quota and the actual carbon emissions, establishing a data center carbon emission model; wherein, the mathematical expression corresponding to the data center carbon emission model is: In Equation (9), E grid,t is the equivalent carbon emission of electricity purchased from the power grid; P grid,t is the output power of the power grid; E sum,t , E t are the total carbon emission and the actual carbon emission respectively; E dc,t is the carbon quota value of the data center; a2, b2, c2 are the carbon emission calculation coefficients of thermal power units; Step S4.3: Establish a carbon trading model for the data center by combining the deviation cost between the carbon emission quota and the actual carbon emissions; wherein, the mathematical expression corresponding to the carbon trading model of the data center is: In Equation (10), σ is the carbon trading base price; δ is the compensation coefficient; θ is the carbon price growth coefficient; d is the length of the carbon trading interval.

6. The multi-layer dynamic energy control method for a hydrogen-doped integrated energy system considering spatio-temporal information integration according to claim 5, characterized in that In Step S5, the mathematical expression corresponding to the step of incorporating the carbon trading cost into the objective function to minimize the system economic cost and carbon emissions as multiple objectives is: In Equation (11), F e is the cost on the energy supply side; F grid is the cost of purchasing electricity from the power grid; F gas is the cost of purchasing gas; F qf is the cost of curtailed wind; F qg is the cost of curtailed solar; F op is the operating cost; F el is the operating cost of the electrolyzer; F mr is the operating cost of the methanation reactor; F fuel is the fuel cost of the carbon capture power plant; F cs is the cost of carbon storage; F cbuy is the cost of purchasing CO2 externally; F DR is for demand response settlement fees; β1 and β2 represent different weight coefficients; In Step S5, the hydrogen-blended integrated energy system scheduling model including the data center includes: the hydrogen-blended gas turbine equipment model, the hydrogen-blended gas boiler equipment model, the data center workload model, the data center energy consumption model, the data center demand response model, the data center server constraint model, the data center maximum delay time constraint model, the hydrogen-blended unit carbon emission model, the data center carbon emission model, the data center carbon trading model, and the mathematical expression with the system economic cost and carbon emissions minimized as multiple objectives.

Images