A data center multi-energy coordinated optimization scheduling method and device

Abstract

The application discloses a kind of data center multi-energy coordination optimization scheduling method and device.It includes: based on the multi-energy system of data center, system operation model and system random fault library are constructed;According to the system random fault library and the system operation model, with the minimum system load shedding amount as target, the reliability baseline of the multi-energy system normal operation is obtained;According to system random fault library, the system operation model and the reliability baseline, with the minimum comprehensive operation cost as target, the optimal multi-energy scheduling strategy of the multi-energy system is obtained.The application can provide multi-energy optimization scheduling strategy considering reliability, flexibility and economy for data center multi-energy system, and optimize the energy scheduling of data center.

Description

Technical Field

[0001] This invention relates to the field of multi-energy scheduling technology, and in particular to a method and apparatus for multi-energy coordinated optimization scheduling in data centers. Background Technology

[0002] With the advancement of the global energy transition, an energy system characterized by low carbon, cleanliness, and efficiency is gradually replacing the traditional fossil fuel supply model. Against this backdrop, data centers, as core infrastructure of the information age, are facing increasingly significant energy consumption issues. The energy consumption of a typical data center is equivalent to the combined energy consumption of tens of thousands of households. Therefore, how to achieve energy-saving retrofits of data centers and reduce their power consumption, cooling consumption, and carbon emissions has become an important research topic.

[0003] To address the energy challenges of data centers, researchers have begun to focus on the integrated utilization of various energy forms, including hydrogen, electricity, cooling, and heating. Among these, hydrogen energy, with its high energy density, ease of storage, and high efficiency and cleanliness, shows great potential in data center energy systems. Fuel cell combined heat and power (CHP) technology, due to its high efficiency and cleanliness, can effectively match the load demands of data centers. Meanwhile, the large-capacity fire-fighting water tanks typically equipped in data centers, if effectively utilized, can avoid resource waste and water pollution problems. Therefore, multi-energy systems have become a feasible solution to meet the electricity and cooling needs of data centers while achieving near-zero carbon emissions.

[0004] However, existing scheduling methods for the specific load demands of data centers still have many shortcomings. On the one hand, data centers have extremely high requirements for operational reliability, and existing methods often focus on equipment redundancy to ensure business continuity and data security, but these methods can easily lead to over-investment and resource waste. On the other hand, although hydrogen-based multi-energy systems have significant advantages, existing research lacks in-depth discussion on their specific application effects in data centers and how to effectively integrate them with existing facilities to save costs. This indicates that there is still room for improvement in the planning and scheduling methods of multi-energy systems in data centers, and further optimization is needed to balance reliability and economy. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method and apparatus for multi-energy coordinated optimization scheduling of data centers, with the aim of optimizing multi-energy scheduling of data centers under the premise of reliable scheduling, and balancing the reliability, flexibility and economy of multi-energy scheduling of data centers.

[0006] To achieve the above objectives, according to a first aspect of the present invention, a data center multi-energy coordinated optimization scheduling method is provided, comprising:

[0007] Based on a data center-based multi-energy system, a system operation model and a system random fault database are constructed.

[0008] Based on the system's random fault library and the system's operating model, with the goal of minimizing the system's load shedding, a reliability baseline for the normal operation of the multi-energy system is obtained.

[0009] Based on the system's random fault library, the system's operating model, and the reliability baseline, the optimal multi-energy scheduling strategy for the multi-energy system is obtained with the goal of minimizing overall operating costs.

[0010] Furthermore, the multi-energy system involves multiple energy sources and includes energy devices corresponding to each of these energy sources;

[0011] The system operation model includes the individual equipment operation model of each energy device in the multi-energy system, and the energy coupling model between the energy devices;

[0012] The system random fault library includes multiple device state time series matrices, and each device state time series matrix includes the state time series vector of each energy device.

[0013] The equipment operation model includes the energy conversion and transfer relationships and operational constraints of each of the energy devices; the energy coupling model includes the balance constraints of various energy sources.

[0014] Furthermore, the system's random fault library is constructed, including:

[0015] Obtain the failure rate and repair rate of each of the energy devices;

[0016] A first threshold for the corresponding energy equipment is determined based on the failure rate and the repair rate. Second threshold , , indicating the first time in the preset duration One time step;

[0017] Generate the energy device corresponding to the first The random number corresponding to each time step According to the energy equipment in the first Each time step state and the random number With the first threshold Or the second threshold The comparison results determine that the energy device is in the first... The state at each time step ;

[0018] According to each of the aforementioned energy devices The states of each time step are used to construct the corresponding state timing vectors, thereby obtaining the device state timing matrix.

[0019] Furthermore, according to the energy device in the first Each time step state and the random number With the first threshold Or the second threshold The comparison results determine that the energy device is in the first... The state at each time step Calculate according to the following formula:

[0020] ,

[0021] .

[0022] Furthermore, the reliability baseline includes the equipment reliability baseline;

[0023] Based on the system's random fault library and the system's operating model, and with the goal of minimizing system load shedding, the reliability baseline for the normal operation of the multi-energy system is obtained, including:

[0024] Using the minimum system load shedding as the first objective function, and with the equipment operation model and the energy coupling model as constraints, a mixed integer linear programming method is used to solve for the equipment reliability baseline.

[0025] The first objective function is calculated according to the following formula:

[0026] ;

[0027] in, , and These are the cut-off amounts for electrical load, thermal load, and cold load, respectively.

[0028] Furthermore, the overall operating cost includes operating cost, flexibility penalty cost, and load shedding penalty cost; the optimal multi-energy dispatch strategy is the output of each of the energy devices when the overall operating cost is minimized.

[0029] Based on the system's random fault library, the system's operating model, and the reliability baseline, and with the objective of minimizing overall operating cost, the optimal multi-energy scheduling strategy for the multi-energy system is obtained, including:

[0030] Taking the minimum overall operating cost as the second objective function, and using the equipment operation model, the energy coupling model, and the equipment reliability baseline as constraints, the optimal multi-energy scheduling strategy of the multi-energy system is solved by a mixed integer linear programming method.

[0031] The second objective function is calculated according to the following formula:

[0032] ;

[0033] in, It is the total operating cost of all energy devices in the multi-energy system. The flexibility penalty cost of the aforementioned multi-energy system It is the load shedding power of the multi-energy system; , These are the flexibility penalty factor and the load shedding penalty factor, respectively; the flexibility penalty cost is obtained based on the equipment reliability baseline of each of the energy devices.

[0034] According to a second aspect of the present invention, a data center multi-energy coordinated optimization scheduling device is provided, comprising:

[0035] The model building module is used to build system operation models and system random fault libraries for multi-energy systems based on data centers;

[0036] The baseline calculation module is used to obtain the reliability baseline for the normal operation of the multi-energy system based on the system random fault library and the system operation model, with the goal of minimizing the system load shedding.

[0037] The scheduling decision module is used to obtain the optimal multi-energy scheduling strategy for the multi-energy system based on the system's random fault library, the system's operating model, and the reliability baseline, with the goal of minimizing the overall operating cost.

[0038] According to a third aspect of the present invention, a computer-readable storage medium is provided having a program stored thereon that, when executed by a processor, implements the steps of the data center multi-energy coordinated optimization scheduling method as described in the first aspect of the present invention.

[0039] According to a fourth aspect of the present invention, a terminal device is provided, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the data center multi-energy coordinated optimization scheduling method as described in the first aspect of the present invention.

[0040] According to a fifth aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps of the data center multi-energy coordinated optimization scheduling method as described in the first aspect of the present invention.

[0041] The embodiments of the present invention have at least one of the following advantages or beneficial effects:

[0042] This invention addresses the high reliability requirements of multi-energy systems in data centers by establishing a system random fault database and a system operation model to determine a reliability baseline. Based on this baseline, the optimal multi-energy scheduling strategy for the data center multi-energy system is determined, thereby optimizing multi-energy scheduling in the data center under the premise of reliable scheduling and balancing the reliability, flexibility, and economy of multi-energy scheduling in the data center.

[0043] This invention generates a device state timing matrix covering the entire lifecycle based on a Monte Carlo-Markov hybrid model, and then solves for the maximum output of each energy device (equipment reliability baseline) with the goal of minimizing system load shedding. By determining this baseline, the minimum reserve capacity required to maintain the data center's critical load under any single or multi-point failure is quantified, making redundancy design change from empirical values ​​to calculable and verifiable deterministic values, thereby providing a foundation for the reliable scheduling of multi-energy systems in data centers.

[0044] This invention, by quantifying the potential load shedding risk and insufficient flexibility risk caused by energy equipment deviating from the reliability baseline, aims to minimize the comprehensive operating cost, including operation cost, flexibility penalty cost, and load shedding penalty cost. It optimizes the solution to obtain the optimal output of each energy device in the data center multi-energy system, balancing economy and flexibility while ensuring reliability.

[0045] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0046] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0047] Figure 1 This is a schematic diagram of the main process of the data center multi-energy coordinated optimization scheduling method according to an embodiment of the present invention.

[0048] Figure 2 This is an energy coupling diagram of a data center multi-energy system according to an embodiment of the present invention.

[0049] Figure 3 This is a Markov two-state transition diagram according to an embodiment of the present invention.

[0050] Figure 4This is a schematic diagram of the working status of energy equipment in the system random fault database of this invention.

[0051] Figure 5 This is a typical summer electricity load curve according to an embodiment of the present invention.

[0052] Figure 6 This is a typical daily cooling load curve for summer according to an embodiment of the present invention.

[0053] Figure 7 This is a typical daily heat load curve for summer according to an embodiment of the present invention.

[0054] Figure 8 This is a schematic diagram of the flexibility margin above the reliability baseline of an embodiment of the present invention.

[0055] Figure 9 This is a schematic diagram of the simulation results of the power system flexibility on a typical summer day according to Strategy 1 of the present invention.

[0056] Figure 10 This is a schematic diagram of the simulation results of the flexibility of the power system on a typical summer day on the reliability baseline of Strategy 2 of this invention.

[0057] Figure 11 This is a schematic diagram of the main modules of the data center multi-energy coordinated optimization scheduling device according to an embodiment of the present invention.

[0058] Figure 12 This is a schematic diagram of the composition of a terminal device according to an embodiment of the present invention. Detailed Implementation

[0059] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0060] Example 1

[0061] Figure 1 This is a schematic diagram of the main flow of a data center multi-energy coordinated optimization scheduling method according to an embodiment of the present invention, as shown below. Figure 1 As shown, the data center multi-energy coordinated optimization scheduling method of this embodiment includes the following steps S101 to S103:

[0062] Step S101: Based on the multi-energy system of the data center, construct a system operation model and a system random fault library;

[0063] Step S102: Based on the system random fault library and the system operation model, with the goal of minimizing the system load shedding, obtain the reliability baseline for the normal operation of the multi-energy system.

[0064] Step S103: Based on the system random fault library, the system operation model, and the reliability baseline, the optimal multi-energy scheduling strategy for the multi-energy system is obtained with the goal of minimizing the overall operating cost.

[0065] Specifically, in this embodiment and some embodiments of the present invention, the multi-energy system involves multiple energy sources, such as electrical energy, chemical energy, thermal energy, and cold energy, and includes energy devices corresponding to each of these energy sources; the system operation model includes the individual operation models of each energy device in the multi-energy system and the energy coupling model between the energy devices; the system random fault library includes multiple device state time series matrices, each of which includes the state time series vector of each energy device; the device operation model includes the energy conversion and transfer relationships and operation constraints of each energy device; and the energy coupling model includes the balance constraints of each of the energy sources.

[0066] Figure 2 This is an energy coupling diagram of a data center multi-energy system according to an embodiment of the present invention. Specifically, as shown... Figure 2 As shown in this embodiment and some embodiments of the present invention, the energy equipment of the multi-energy system of the data center includes a gas internal combustion engine, a waste heat boiler, an absorption chiller, an electric chiller, photovoltaic power generation equipment, a heat pump, a storage battery, and a cold storage device, etc.

[0067] Specifically, in this embodiment and some embodiments of the present invention, the operating models of various energy devices are described below.

[0068] (1) Operating model of gas internal combustion engine

[0069] This includes the energy conversion and transfer relationships of a gas-fired internal combustion engine and its operational constraints. Specifically, the energy conversion relationships of a gas-fired internal combustion engine are as follows:

[0070] ;

[0071] in, This indicates that the gas internal combustion engine is at time step ; output electrical power; This indicates that the gas internal combustion engine is at time step Power consumption of natural gas; This indicates that the gas internal combustion engine is at time step The volume of natural gas consumed by the power plant; Indicates the calorific value of natural gas; This indicates that the gas internal combustion engine is at time step Waste heat recovery power; This indicates the power generation efficiency of the gas-fired internal combustion engine; This indicates the waste heat recovery efficiency of the gas-fired internal combustion engine.

[0072] Operating constraints for gas-fired internal combustion engines include power constraints and ramp constraints.

[0073] The power constraint of a gas-fired internal combustion engine is:

[0074] ;

[0075] in, , These represent the minimum operating power and rated operating power of the gas-fired internal combustion engine, respectively.

[0076] Gas-fired internal combustion engine ramp rate:

[0077] ;

[0078] in, , These represent the uphill and downhill climbing rates of a gas-fired internal combustion engine, respectively.

[0079] (2) The operation model of the waste heat boiler, including the energy conversion and transfer relationship of the waste heat boiler, specifically:

[0080] ;

[0081] The waste heat boiler recovers a portion of the waste heat from the gas-fired internal combustion engine. This indicates that the waste heat boiler is in time step Thermal output power; This indicates the heat recovery efficiency of the waste heat boiler.

[0082] The operational constraints of waste heat boilers are already implicit in the energy conversion relationship and do not require special constraints.

[0083] (3) The operating model of the absorption chiller, including the energy conversion relationship and operating constraints of the absorption chiller. The energy conversion relationship of the absorption chiller is as follows:

[0084] ;

[0085] in, Indicates the refrigeration capacity of the absorption chiller. This indicates the refrigeration efficiency of the absorption chiller. This indicates that the absorption chiller is at time step The input thermal power.

[0086] The operating constraints of an absorption chiller include the absorption chiller power constraints and the absorption chiller ramp constraints:

[0087] The power constraint for absorption chillers is:

[0088]

[0089] in, , These represent the minimum operating power and rated operating power of the absorption chiller, respectively.

[0090] Absorption chiller ramp rate:

[0091]

[0092] in, , These represent the upward ramp rate and downward ramp rate of the absorption chiller, respectively.

[0093] (4) The operating model of the electric chiller, including the energy conversion relationship and operating constraints of the electric chiller. Specifically, the energy conversion relationship of the electric chiller is as follows:

[0094] ;

[0095] in, This indicates that the electric chiller is at time step The cooling capacity; This indicates that the electric chiller is at time step The input electrical power; This indicates the refrigeration efficiency of the electric chiller.

[0096] Electric chiller operating constraints include electric chiller power constraints and electric chiller ramp constraints:

[0097] The power constraint of the electric chiller is:

[0098] ;

[0099] in, , These represent the minimum operating power and rated operating power of the electric chiller, respectively.

[0100] Electric chiller ramp rate:

[0101]

[0102] in, , These represent the upward and downward ramp rates of the electric chiller, respectively.

[0103] (5) The operating model of the photovoltaic power generation equipment, including the energy conversion relationship of the photovoltaic power generation equipment. Specifically, the energy conversion relationship of the photovoltaic power generation equipment is as follows:

[0104] ;

[0105] in, This indicates that the photovoltaic power generation equipment is at time step ; output power; Indicates the photovoltaic power loss coefficient; This indicates the number of photovoltaic panels in the photovoltaic power generation equipment; Indicates at time step Light intensity; This indicates that the photovoltaic power generation equipment is at time step The temperature of the photovoltaic panel; This indicates the photovoltaic output power of the photovoltaic power generation equipment under standard conditions; Indicates the light intensity under standard conditions; This indicates the temperature of the photovoltaic panel under standard conditions. This represents the temperature coefficient.

[0106] (6) The heat pump operation model, including the heat pump energy conversion relationship and heat pump operation constraints. Specifically, the heat pump energy conversion relationship is as follows:

[0107] ;

[0108] in, This indicates that the heat pump is in time step The heating power; This indicates that the heat pump is in time step The cooling capacity; This represents the coefficient of performance (COP) of the heat pump. This represents the coefficient of performance (COP) of the heat pump. This indicates that the heat pump is operating at a certain time step. Required electrical power; This indicates that the heat pump cools at time step The required electrical power.

[0109] Heat pump operating constraints include heat pump power constraints and heat pump ramping constraints:

[0110] Heat pump power constraint is

[0111]

[0112] in, , These represent the rated operating power of the heat pump for heating and the rated operating power for cooling, respectively.

[0113] Heat pump ramp rate:

[0114]

[0115] in, , These represent the upward ramp rate and downward ramp rate of the heat pump heating system, respectively. , These represent the upward ramp rate and downward ramp rate of heat pump cooling, respectively.

[0116] (7) The battery operation model, including the battery energy conversion relationship and battery operation constraints. Specifically, the battery energy conversion relationship is as follows:

[0117] ;

[0118] in, , The batteries mentioned above are in time step and time step Internal energy storage capacity This indicates the self-discharge rate (loss rate) of the battery. This indicates the charging rate of the battery; This indicates the discharge rate of the battery; This indicates the charging efficiency of the battery; This indicates the discharge efficiency of the battery; This indicates the length of a unit time step.

[0119] The operating constraints of a battery include its charging constraints and discharging constraints:

[0120] ;

[0121] in, , These represent the rated charging efficiency and discharging efficiency of the battery, respectively.

[0122] (8) The operating model of the cold storage device, including the energy conversion relationship of the cold storage device, is as follows:

[0123] ;

[0124] in, , The water storage tanks are respectively in time step and time step Internal cold storage capacity; This indicates the cold loss rate in the water storage tank; This indicates that the water storage tank is at time step The cold storage capacity; This indicates that the water storage tank is at time step Cooling power.

[0125] The operational constraints of the cold storage device are already implicit in the energy conversion relationship, and no special constraints are imposed.

[0126] Specifically, in this embodiment and some embodiments of the present invention, the energy coupling model between energy devices in a data center multi-energy system includes an electrical balance coupling model, a thermal balance coupling model, and a cold balance coupling model.

[0127] (1) The electrical balance coupling model is as follows:

[0128]

[0129] in, Indicates at time step The power purchase capacity, Indicates at time step electrical load, Indicates at time step Electricity sales capacity;

[0130] (2) The thermal equilibrium coupling model is:

[0131]

[0132] in, Indicates at time step The heat load;

[0133] (3) The cold equilibrium coupling model is:

[0134]

[0135] in, Indicates at time step The cooling load.

[0136] Specifically, in this embodiment and some embodiments of the present invention, a Markov process is used to construct a system random fault library for devices in a data center multi-energy system. Figure 3 This is a Markov two-state transition diagram according to an embodiment of the present invention. It can be understood that... Figure 3 As shown, in this embodiment and some embodiments of the present invention, each energy device has two states: normal state 0 and fault state 1; , They represent the first The failure rate and repair rate corresponding to each energy device. Each energy device at any time right neighbor State transition probability It can be represented as:

[0137]

[0138] in, Represents moments in the continuous time domain. Indicates the first The probability that an energy device transitions from state 0 to state 1. Indicates the first The probability that an energy device transitions from state 1 to state 0. Indicates the first The probability that an energy device transitions from state 0 to state 0. Indicates the first The probability that an energy device transitions from state 1 to state 1.

[0139] No. The shutdown process of an energy device It is a random process, the first... Each energy device at any time The state of being Represented as The probability, According to Markov process theory, it can be deduced that:

[0140] ,

[0141] Furthermore, it can be obtained

[0142] ,

[0143] Solving the above two equations, we can obtain the following:

[0144] ,

[0145] Using the above formula, the probability (or threshold) of each energy device maintaining normal state 0 and fault state 1 at any given time can be calculated. Furthermore, it can be understood that at each time step in the discrete time domain... The probability (or threshold) of maintaining normal state 0 and maintaining fault state 1. , Time is increasing step by step.

[0146] Specifically, in this embodiment and some embodiments of the present invention, the construction of the system random fault library in step S101 includes steps S101a to S101d:

[0147] Step S101a: Obtain the failure rate and repair rate of each of the energy devices;

[0148] Step S101b: Determine the first threshold for the corresponding energy device based on the failure rate and the repair rate. Second threshold , , indicating the first time in the preset duration One time step;

[0149] Step S101c, generating the corresponding energy device in the first step. The random number corresponding to each time step According to the energy equipment in the first Each time step state and the random number With the first threshold Or the second threshold The comparison results determine that the energy device is in the first... The state at each time step ;

[0150] Step S101d, according to each of the energy devices in The states of each time step are used to construct the corresponding state timing vectors, thereby obtaining the device state timing matrix.

[0151] Among them, the Each time step state The value includes normal state 0 and fault state 1; the first threshold It is the first The threshold for maintaining the energy equipment in a normal state of 0 at each time step; the second threshold It is the first The threshold for maintaining the energy equipment in fault state 1 at each time step; .

[0152] Specifically, in this embodiment and some embodiments of the present invention, in step S101b, the first threshold of the corresponding energy device is determined according to the following formula. Second threshold :

[0153] ,

[0154] in, It is the duration of each time step. , These represent the failure rate and repair rate of the energy equipment, respectively. It is understood that the failure rate and repair rate may be the same or different for different energy equipment. The above formula does not specify the identification of the energy equipment. However, this will not lead to misunderstandings, as the failure rate and repair rate of each of the energy devices mentioned are the same.

[0155] Specifically, in this embodiment and some embodiments of the present invention, when constructing the system random fault library, step S101c determines the energy device in the first... The state at each time step :

[0156] ,

[0157] .

[0158] Specifically, in this embodiment and some embodiments of the present invention, in step S101d, the generation of the system random fault database involves repeating steps S101a to S101c for all energy devices in the system throughout the entire preset time period. This yields a multidimensional state-time series matrix. Each row of the state-time series matrix represents the state of all devices at a given time step, and each column represents the state evolution history of a single device throughout the entire simulation cycle. Simulating multiple state-time series matrices constitutes a database of device failure scenarios for subsequent analysis. The state-time series matrices are shown in Table 1, where a schematic diagram of the operating state of energy device 45 for a specific state-time series matrix is ​​shown below. Figure 4 As shown, the horizontal axis represents the time step, and the vertical axis represents the state.

[0159] Table 1

[0160]

[0161] It is understood that in this embodiment and some embodiments of the present invention, the failure scenarios of the multi-energy system include three categories: the first category is a scenario where the energy equipment is fault-free and the multi-energy system is operating normally; the second category is a scenario where the energy equipment is faulty but the multi-energy system is operating normally; and the third category is a scenario where the energy equipment is faulty and the multi-energy system needs to cut load. The reliability baseline includes the equipment reliability baseline and the system reliability baseline. Corresponding to each of the state timing matrices in the first and second categories, the maximum output of the energy equipment is the equipment reliability baseline of the energy equipment. Corresponding to each of the state timing matrices in the second category, the maximum value of the sum of the equipment reliability baselines of all fault-free energy equipment is the system reliability baseline.

[0162] Specifically, in this embodiment and some embodiments of the present invention, the system reliability baseline is calculated according to the following formula:

[0163] ;

[0164] in, Indicates the system reliability baseline. Indicates the first The equipment reliability baseline for each energy device.

[0165] Specifically, in this embodiment and some embodiments of the present invention, step S102 includes: taking the minimum system load shedding as the first objective function, and using the equipment operation model and the energy coupling model as constraints, solving the equipment reliability baseline and the system reliability baseline using a mixed integer linear programming method.

[0166] The first objective function is calculated according to the following formula:

[0167] ;

[0168] in, , and These are the cut-off amounts for electrical load, thermal load, and cold load, respectively.

[0169] Figure 5 , Figure 6 and Figure 7 These are, respectively, the typical summer daily electricity load curve, cooling load curve, and heating load curve of this invention. Specifically, in this embodiment and some embodiments of this invention, based on... Figures 6-7 Based on the data shown, the above system operation model, and the system random failure implementation step S102, the equipment reliability baseline and the system reliability baseline for normal operation of the multi-energy system are obtained.

[0170] Specifically, in this embodiment and some embodiments of the present invention, the comprehensive operating cost includes operating cost, flexibility penalty cost, and load shedding penalty cost; the optimal multi-energy scheduling strategy is the output of each of the energy devices when the comprehensive operating cost is minimized; step S103 includes: taking the minimum comprehensive operating cost as the second objective function, taking the device operation model, the energy coupling model, and the device reliability baseline as constraints, and using a mixed integer linear programming method to solve the optimal multi-energy scheduling strategy of the multi-energy system.

[0171] The second objective function is calculated according to the following formula:

[0172] ;

[0173] in, It is the total operating cost of all energy devices in the multi-energy system. This is the flexibility penalty cost of the multi-energy system; It is the load shedding power of the multi-energy system; , These are the flexibility penalty factor and the load shedding penalty factor;

[0174] Specifically, in this embodiment and some embodiments of the present invention, the total operating cost of all energy devices in the multi-energy system is... Calculate using the following formula:

[0175]

[0176] in, This represents the total operation and maintenance cost of energy equipment. This indicates the cost of electricity purchase for a multi-energy system. Indicates the cost of natural gas. This indicates the cost of carbon emissions.

[0177] Specifically, in this embodiment and some embodiments of the present invention, the calculations are performed according to the following formulas:

[0178] ;

[0179] ;

[0180] ;

[0181] ;

[0182] in, Indicates the first The operation and maintenance costs of individual energy equipment Indicates the first Each energy device in time step The power; This indicates the time-of-use electricity price. This indicates the time-of-use electricity price. Indicates the price of natural gas; Indicates the volume of natural gas consumed; This represents the unit carbon emission cost coefficient; This represents the carbon emission conversion factor for the electricity purchasing system. This indicates the carbon emission coefficient of a gas-fired internal combustion engine.

[0183] Specifically, in this embodiment and some embodiments of the present invention, optimal multi-energy scheduling is achieved on the baseline of equipment reliability. More specifically, it involves scheduling each energy device at each time step. By maintaining the capacity between the actual output and the reliability baseline, the flexibility margin of each device can be obtained, including increasing and decreasing the flexibility margin, and achieving optimal multi-energy scheduling based on the flexibility margin.

[0184] Figure 8 This is a schematic diagram illustrating the flexibility margin above the device reliability baseline according to an embodiment of the present invention. For details, see [link to specific documentation]. Figure 8 Taking GE's gas-fired internal combustion engine as an example, its maximum output required in all failure scenarios is used as the "reliability baseline." The gas-fired internal combustion engine at time steps... Power generation from arrive This capacity is a "backup" that must be reserved to ensure reliability. Therefore, when At that time, it can use the increased flexibility margin. for:

[0185] ;

[0186] The equipment can be adjusted with reduced flexibility margin. for: ;

[0187] when At that time, the equipment can be used to increase the flexibility margin. for:

[0188] ;

[0189] The equipment can be adjusted with reduced flexibility margin. for:

[0190] ;

[0191] in, , These are the maximum and minimum operating power of a gas-fired internal combustion engine.

[0192] Therefore, in this embodiment and some embodiments of the present invention, the flexibility penalty cost is calculated according to the following formula:

[0193] ;

[0194] in, This indicates an upward adjustment of the flexibility deficit; This indicates a reduction in flexibility deficit; This indicates an upward adjustment of the flexibility penalty coefficient for flexibility deficits; This indicates a reduction in the flexibility penalty coefficient for flexibility deficits.

[0195] Specifically, in this embodiment and some embodiments of the present invention, when solving for the flexibility penalty cost, the constraints obtained based on the reliability baseline include:

[0196] (1) Flexibility and supply-demand constraints:

[0197]

[0198] in, This indicates the upward flexibility required by the system. This indicates the required downward flexibility of the system; This represents the total upward flexibility provided by all devices within the system; This represents the total downward flexibility provided by all devices within the system. In the formula, and These are decision variables, obtained during the calculation process. To ensure economy during operation, the system will minimize the lack of flexibility as much as possible.

[0199] (2) Required flexibility constraints of the system:

[0200]

[0201] in, This represents the upper limit of the day-ahead forecast electrical load at time t; This represents the lower limit of the day-ahead predicted electrical load at time t.

[0202] Understandably, the reliability of a multi-energy system can be summarized as adequacy and security. Adequacy refers to the system's ability to continuously and uninterruptedly supply power to users under steady-state conditions, and is generally described by load shedding probability, load shedding time, and expected power shortage value. Security refers to the system's ability to remain stable after encountering disturbances or faults during transients, and its severity is generally described by indicators such as power outage frequency and power outage duration.

[0203] Specifically, in this embodiment and some embodiments of the present invention, the probability of insufficient power (LOLP) and expected power shortage (EENS) are selected as two indicators to evaluate the reliability of the data center multi-energy system.

[0204] The probability of insufficient power (LOLP), also known as the load shedding probability, reflects the probability that the system's power supply cannot meet load demand. When power supply falls short of demand, load reduction is necessary. LOLP is typically the ratio of the number of load shedding events to the total number of random experiments, calculated using the following formula:

[0205] ;

[0206] in, This represents the total number of random experiments. Each random experiment calls a device state timing matrix from the system's random fault database. , Representing the device state timing matrix The first in The status of each energy device at each time step; The randomized trial function for LOLP is calculated according to the following formula:

[0207] ;

[0208] LOLP is a very important indicator for measuring system reliability. Whether the power supply is sufficient reflects the sufficiency of the system, and the degree of sufficiency is closely related to the system security. LOLP takes into account both the sufficiency and security of reliability evaluation indicators.

[0209] The Expected Energy Shortage (EENS) reflects the expected energy shortage caused by system failures, i.e., the average amount of load shedding over a period of time, embodying the energy shortage in the system. Therefore, the load shedding power of a multi-energy system is calculated according to the following formula:

[0210] ;

[0211] in, Indicates the state of a multi-energy system Cut-off load power (active load removed).

[0212] Specifically, in order to intuitively understand the beneficial effects of the present invention, the present invention is based on Figure 5 , Figure 6 and Figure 7 The data were simulated using the method (strategy 1) and the method (strategy 2) of the present invention, and a comparison was made.

[0213] Specifically, Strategy 1 is as follows: the system does not consider equipment failure scenarios, does not reserve equipment capacity, and only considers the lowest economic cost. The results are obtained through simulation using MATLAB software, as shown below. Figure 9 As shown. Strategy 2 is: considering equipment failure scenarios, reserving equipment capacity, and using the lowest overall economic cost as the objective function. The reliability and flexibility results obtained through MATLAB simulation are shown below. Figure 10 .

[0214] Referring to Tables 2 and 3, compared with Strategy 1, Strategy 2 reduces the total economic cost by 4.16%, the cost of electricity purchase by 5.59%, the cost of gas purchase by 7.03%, and the cost of carbon dioxide treatment by 17.65%. At the same time, it avoids the flexibility deficit penalty and load shedding penalty, improves reliability, and reduces the total economic cost by 7.39%.

[0215] Table 2

[0216]

[0217] Table 3

[0218]

[0219] Example 2

[0220] According to another aspect of the embodiments of the present invention, such as Figure 11As shown, a data center multi-energy coordinated optimization scheduling device is provided, comprising:

[0221] The model building module is used to build system operation models and system random fault libraries for multi-energy systems based on data centers;

[0222] The baseline determination module is used to obtain the reliability baseline for the normal operation of the multi-energy system based on the system random fault library and the system operation model, with the goal of minimizing the system load shedding.

[0223] The scheduling decision module is used to obtain the optimal multi-energy scheduling strategy for the multi-energy system based on the system's random fault library, the system's operating model, and the reliability baseline, with the goal of minimizing the overall operating cost.

[0224] Example 3

[0225] like Figure 12 As shown, Embodiment 3 of the present invention provides a terminal device, including at least one processor and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps in the data center multi-energy coordinated optimization scheduling method as described in the first aspect of the present invention.

[0226] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripherals, voltage regulators, and power management circuits, via interfaces, as is well known in the art. Interfaces provide a connection between the bus and the transceiver, such as communication interfaces or user interfaces. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0227] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0228] Example 4

[0229] Embodiment 4 of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the data center multi-energy coordinated optimization scheduling method as described in the first aspect of the present invention.

[0230] Those skilled in the art will understand from the foregoing description that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes, but is not limited to, various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic storage devices, and optical storage devices.

[0231] Example 5

[0232] Embodiment 5 of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the data center multi-energy coordinated optimization scheduling method as described in the first aspect of the present invention.

[0233] Based on the detailed description of the embodiments of the present invention above, it can be more clearly understood that the present invention has the following advantages:

[0234] This invention addresses the high reliability requirements of multi-energy systems in data centers by establishing a system random fault database and a system operation model to determine a reliability baseline. Based on this baseline, the optimal multi-energy scheduling strategy for the data center multi-energy system is determined, thereby optimizing multi-energy scheduling in the data center under the premise of reliable scheduling and balancing the reliability, flexibility, and economy of multi-energy scheduling in the data center.

[0235] This invention generates a device state timing matrix covering the entire lifecycle based on a Monte Carlo-Markov hybrid model, and then solves for the maximum output of each energy device (equipment reliability baseline) with the goal of minimizing system load shedding. By determining this baseline, the minimum reserve capacity required to maintain the data center's critical load under any single or multi-point failure is quantified, making redundancy design change from empirical values ​​to calculable and verifiable deterministic values, thereby providing a foundation for the reliable scheduling of multi-energy systems in data centers.

[0236] This invention, by quantifying the potential load shedding risk and insufficient flexibility risk caused by energy equipment deviating from the reliability baseline, aims to minimize the comprehensive operating cost, including operation cost, flexibility penalty cost, and load shedding penalty cost. It optimizes the solution to obtain the optimal output of each energy device in the data center multi-energy system, balancing economy and flexibility while ensuring reliability.

[0237] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0238] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-energy coordinated optimization scheduling method for data centers, characterized in that, Based on a data center multi-energy system, a system operation model and a system random fault library are constructed. The multi-energy system involves multiple energy sources and includes energy devices corresponding to each energy source. The system operation model includes the individual operation model of each energy device in the multi-energy system and the energy coupling model between the energy devices. The system random fault library includes multiple device state time series matrices, and each device state time series matrix includes the state time series vector of each energy device. Based on the system's random fault database and the system's operating model, and with the goal of minimizing system load shedding, a reliability baseline for the normal operation of the multi-energy system is obtained. This includes: using the minimum system load shedding as the first objective function, and with the equipment operating model and the energy coupling model as constraints, solving for the equipment reliability baseline using a mixed-integer linear programming method; the first objective function is calculated according to the following formula: ;in, , and These are the cut-off amounts for electrical load, thermal load, and cooling load, respectively; the reliability baseline includes the equipment reliability baseline; the failure scenarios of the multi-energy system include a first type of scenario and a second type of scenario. The first type of scenario is when the energy equipment is fault-free and the multi-energy system is operating normally; the second type of scenario is when the energy equipment is faulty but the multi-energy system is operating normally; corresponding to the time-series matrices of each equipment state in the first type of scenario and the second type of scenario, the maximum output of the energy equipment is the equipment reliability baseline of the energy equipment; Based on the system's random fault library, the system's operating model, and the reliability baseline, the optimal multi-energy scheduling strategy for the multi-energy system is obtained with the goal of minimizing the overall operating cost; the overall operating cost includes the flexibility penalty cost of the multi-energy system. The flexibility penalty cost is calculated according to the following formula: ； in, This indicates an upward adjustment of the flexibility deficit; This indicates a reduction in flexibility deficit; This indicates an increase in the flexibility penalty coefficient for flexibility deficits; This indicates a reduction in the flexibility penalty coefficient for flexibility deficits; The flexibility penalty cost is obtained based on the equipment reliability baseline of each of the energy devices; when solving for the flexibility penalty cost, the constraints obtained based on the equipment reliability baseline include: (1) Flexibility and supply-demand constraints: ； in, This indicates the upward flexibility required by the system. This indicates the required downward flexibility of the system; This represents the total upward flexibility provided by all the energy devices within the system; This represents the total downside flexibility provided by all the energy devices within the system; (2) Required flexibility constraints of the system: ； in, Indicates the first The upper limit of the day-ahead forecast electrical load at each time step; Indicates the first The lower limit of the day-ahead forecast electricity load at each time step. Indicates the first Electrical load at each time step.

2. The method according to claim 1, characterized in that, The equipment operation model includes the energy conversion and transfer relationships and operational constraints of each of the energy devices; the energy coupling model includes the balance constraints of various energy sources.

3. The method according to claim 2, characterized in that, Constructing the system's random fault library includes: Obtain the failure rate and repair rate of each of the energy devices; A first threshold for the corresponding energy equipment is determined based on the failure rate and the repair rate. Second threshold , , indicating the first time in the preset duration Each time step, the formula is as follows: ， in, It is the duration of each time step. , These represent the failure rate and repair rate of the energy equipment, respectively. Generate the energy device corresponding to the first Random numbers corresponding to each time step According to the energy equipment in the first Each time step state and the random number With the first threshold Or the second threshold The comparison results determine that the energy device is in the first... The state at each time step ; According to each of the aforementioned energy devices The states of each time step are used to construct the corresponding state timing vectors, thereby obtaining the device state timing matrix.

4. The method according to claim 3, characterized in that, According to the energy equipment in the Each time step state and the random number With the first threshold Or the second threshold The comparison results determine that the energy device is in the first... The state at each time step Calculate according to the following formula: ， 。 5. The method according to claim 2, characterized in that, The overall operating cost also includes the total operating cost of all energy devices in the multi-energy system and the load shedding power of the multi-energy system; the optimal multi-energy scheduling strategy is the output of each energy device when the overall operating cost is minimized. Based on the system's random fault library, the system's operating model, and the reliability baseline, and with the objective of minimizing overall operating cost, the optimal multi-energy scheduling strategy for the multi-energy system is obtained, including: Taking the minimum overall operating cost as the second objective function, and using the equipment operation model, the energy coupling model, and the equipment reliability baseline as constraints, the optimal multi-energy scheduling strategy of the multi-energy system is solved by a mixed integer linear programming method. The second objective function is calculated according to the following formula: ； in, It is the total operating cost of all energy devices in the multi-energy system. This is the flexibility penalty cost of the multi-energy system; It is the load shedding power of the multi-energy system; , These are the flexibility penalty coefficient and the load shedding penalty coefficient, respectively.

6. A data center multi-energy coordinated optimization scheduling device, characterized in that, include: The model building module is used to construct a system operation model and a system random fault library based on a multi-energy system in a data center. The multi-energy system involves multiple energy sources and includes energy devices corresponding to each energy source. The system operation model includes the individual operation model of each energy device in the multi-energy system and the energy coupling model between the energy devices. The system random fault library includes multiple device state time series matrices, and each device state time series matrix includes the state time series vector of each energy device. The baseline determination module is used to obtain the reliability baseline for the normal operation of the multi-energy system based on the system random fault library and the system operation model, with the goal of minimizing the system load shedding. This includes: using the minimum system load shedding as the first objective function, and with the equipment operation model and the energy coupling model as constraints, solving for the equipment reliability baseline using a mixed-integer linear programming method; the first objective function is calculated according to the following formula: ;in, , and These are the cut-off amounts for electrical load, thermal load, and cooling load, respectively; the reliability baseline includes the equipment reliability baseline; the failure scenarios of the multi-energy system include a first type of scenario and a second type of scenario. The first type of scenario is when the energy equipment is fault-free and the multi-energy system is operating normally; the second type of scenario is when the energy equipment is faulty but the multi-energy system is operating normally; corresponding to each state-time matrix in the first type of scenario and the second type of scenario, the maximum output of the energy equipment is the equipment reliability baseline of the energy equipment; The scheduling decision module is used to obtain the optimal multi-energy scheduling strategy for the multi-energy system based on the system's random fault library, the system's operating model, and the reliability baseline, with the goal of minimizing the overall operating cost; the overall operating cost includes the flexibility penalty cost of the multi-energy system. The flexibility penalty cost is calculated according to the following formula: ； in, This indicates an upward adjustment of the flexibility deficit; This indicates a reduction in flexibility deficit; This indicates an increase in the flexibility penalty coefficient for flexibility deficits; This indicates a reduction in the flexibility penalty coefficient for flexibility deficits; The flexibility penalty cost is obtained based on the equipment reliability baseline of each of the energy devices; when solving for the flexibility penalty cost, the constraints obtained based on the equipment reliability baseline include: (1) Flexibility and supply-demand constraints: ； in, This indicates the upward flexibility required by the system. This indicates the required downward flexibility of the system; This represents the total upward flexibility provided by all the energy devices within the system; This represents the total downside flexibility provided by all the energy devices within the system; (2) Required flexibility constraints of the system: ； in, Indicates the first The upper limit of the day-ahead forecast electrical load at each time step; Indicates the first The lower limit of the day-ahead forecast electricity load at each time step. Indicates the first Electrical load at each time step.

7. A computer-readable storage medium, characterized in that, It stores a program that, when executed by a processor, implements the data center multi-energy coordinated optimization scheduling method as described in any one of claims 1-5.

8. A terminal device, characterized in that, It includes a memory, a processor, and a program stored in the memory and capable of running on the processor, wherein when the processor executes the program, it implements the data center multi-energy coordinated optimization scheduling method as described in any one of claims 1-5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the data center multi-energy coordinated optimization scheduling method according to any one of claims 1-5.

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