Energy station resilience promotion planning method and system considering post-earthquake data center response

Abstract

The present application relates to a kind of energy station elasticity promotion planning method and system considering post-earthquake data center response, the method includes the following steps: obtaining energy station preset parameter, establish the fault model of multiple energy stations under earthquake disaster;Based on the constraint condition containing post-earthquake energy station load shedding upper limit constraint and data center internal temperature constraint, construct the energy station elasticity promotion planning model considering post-earthquake data center response characteristics, the data center response characteristics include data center energy consumption characteristics, waste heat recovery characteristics and energy space-time adjustable characteristics;The energy station elasticity promotion planning model is solved, and the sizing scheme of the energy equipment of multiple energy stations is obtained, and the planning scheme of energy interconnection pipeline between energy station.Compared with prior art, the present application has the advantages of reducing the disaster recovery capacity of energy station to deal with earthquake disaster, improving energy station planning economy and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy station elastic promotion planning, and in particular to an energy station elastic promotion planning method and system considering post-earthquake data center response. BACKGROUND

[0002] The serious threat of energy and environmental problems all over the world has brought major changes to the way energy is produced and consumed. Integrated energy systems, with their ability to complement and cooperate with multiple energies, greatly improve energy utilization efficiency and promote the development of sustainable energy, and have become one of the research hotspots at home and abroad. Integrated energy systems have a complex structure, mainly including energy stations, energy distribution networks and loads. Energy stations are composed of energy generation, conversion and storage devices, and provide energy supply for loads. As a key node of energy supply, the importance of the elastic promotion planning of energy stations is increasingly prominent.

[0003] Natural disasters such as earthquakes, floods, hurricanes and other unpredictable events pose a serious threat to the normal operation of energy stations. These disasters can cause damage to energy station facilities, energy supply interruptions and other problems, and have a serious impact on economic and social development. Therefore, improving the elasticity of energy stations so that they can quickly recover after natural disasters occurs is of great significance to ensuring energy supply safety.

[0004] However, the existing technology for energy station elastic promotion planning usually only considers the impact of energy station devices, and the planning reliability needs to be improved. SUMMARY

[0005] The present application is to overcome the defects of the prior art and provide an energy station elastic promotion planning method and system considering post-earthquake data center response, which can reduce the disaster recovery capacity of energy stations in response to earthquake disasters and improve the economic efficiency of energy station planning.

[0006] The object of the present application can be achieved by the following technical solutions:

[0007] An energy station elastic promotion planning method considering post-earthquake data center response, comprising the following steps:

[0008] Obtain the preset parameters of the energy station, and establish a multi-energy station failure model under earthquake disasters;

[0009] Based on the constraint conditions including the post-earthquake energy station load shedding upper limit constraint and the data center internal temperature constraint, an energy station elastic promotion planning model considering the response characteristics of the data center is constructed, the response characteristics of the data center including the energy consumption characteristics, the waste heat recovery characteristics and the energy use space-time adjustment characteristics;

[0010] Solving the energy station elastic promotion planning model, a selection and capacity determination scheme of energy equipment of multiple energy stations and a planning scheme of energy interconnection pipelines between energy stations are obtained.

[0011] Further, the preset parameters of the energy station include an energy station fortification earthquake intensity, an energy station to-be-selected device type and a device earthquake vulnerability function.

[0012] Further, the energy station failure model is represented as:

[0013]

[0014] In the formula: is a set of energy station failure states; represents a set of damaged device type combinations u; represents a set of damaged device quantities in different states; is a set of probabilities of different failure states; represents the probability of the zth failure state; represents a set of damaged device type combinations u in the zth failure state; represents the probability that the ith device is in the most severely damaged state under the earthquake disaster; o represents the order of the energy station failure set; u is a combination of device failure types under the order o of the failure state; V represents a set of energy equipment types, and u is a subset of the set V; Card (u) represents the number of damaged device types.

[0015] Further, when the energy station elastic promotion planning model is constructed, the internal temperature dynamic change and the data center waste heat recovery characteristics are considered, the time and space adjustable characteristics of data load are used to coordinate energy flow and data flow, the time and space distribution of the multi-energy load of the post-earthquake energy station is remodeled, and the supply and demand balance of multiple energy stations is changed.

[0016] Further, the waste heat recovery characteristics are characterized by a data center waste heat recovery model, and the data center waste heat recovery model is:

[0017]

[0018] In the formula: is the electric energy consumed for improving the energy quality of the data center waste heat; is the thermal energy consumed for improving the energy quality of the data center waste heat; α DC is a data center waste heat recovery coefficient; is a data center waste heat recovery power; R DC and S DC are a heat exchange coefficient and a heat exchange area of the data center, respectively; and are an external environment temperature and an internal temperature of the data center, respectively; Input cold power to the data center; ρ air and C air These are the density and heat capacity of air, respectively; V DC P represents the volume of the data center; t and T represent the time period number and corresponding set within the scheduling cycle, respectively; P h,DC,t The heat power generated during the data load processing in the data center.

[0019] Furthermore, the spatiotemporal adjustable energy consumption characteristics are characterized by a time-balanced model for delay-tolerant data loads and a spatial-balanced model for delay-sensitive data loads.

[0020] Furthermore, the energy consumption characteristics of the data center include the energy consumption of information technology equipment and the energy consumption of cooling equipment.

[0021] Furthermore, the upper limit constraint for load shedding at the post-earthquake energy station is as follows:

[0022]

[0023] In the formula: For the nth time after the earthquake disaster ES The load shedding capacity of the m-th energy source at each energy station; ε sei The upper limit for multi-energy load shedding at multi-energy stations after an earthquake disaster; m and Ω m These are the energy type's number and set, respectively; n ES and Ω ES These are the energy station's number and set, respectively.

[0024] Furthermore, the energy station resilient upgrade planning model takes the minimum annual comprehensive cost as its objective function, and the constraints of the energy station resilient upgrade planning model also include constraints on the operating characteristics of energy equipment, energy station energy purchase constraints, energy connection pipeline transmission capacity constraints, and power balance constraints.

[0025] Furthermore, the energy station resilience upgrade planning model is solved using the branch and bound method of the commercial solver Gurobi.

[0026] The present invention also provides an energy station resilience upgrade planning system that takes into account the post-earthquake data center response, including one or more processors, a memory, and one or more programs stored in the memory, the one or more programs including instructions for executing the energy station resilience upgrade planning method that takes into account the post-earthquake data center response as described above.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] (1) The present application is based on constraint conditions including post-earthquake energy station load shedding upper limit constraint and data center internal temperature constraint, and constructs an energy station elastic lifting planning model considering the response characteristics of the data center after the earthquake, considers the data center response characteristics such as data center energy consumption characteristics, waste heat recovery characteristics and energy consumption space-time adjustable characteristics, and can effectively improve the unit capacity utilization rate of energy equipment during normal operation and reduce the operation cost of multiple energy stations.

[0029] (2) The present application considers the space-time adjustable characteristics and waste heat recovery characteristics of the data center energy consumption, and can reshape the space-time distribution of multiple energy loads based on data load balancing after the earthquake disaster, change the supply and demand balance of multiple energy stations, and improve the overall elasticity of interconnected multiple energy stations in response to earthquake disasters. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 The flowchart of the present application is shown in the figure;

[0031] Figure 2 The energy station multi-energy load curve of the embodiment is shown in the figure;

[0032] Figure 3 The data center data load of the embodiment is shown in the figure;

[0033] Figure 4 The post-disaster allocation scheme of the delay-sensitive data load of the typical day in winter is shown in the figure;

[0034] Figure 5 The post-disaster allocation scheme of the delay-tolerant data load of the typical day in winter is shown in the figure;

[0035] Figure 6 The post-disaster internal thermal power balance and temperature change of the No. 2 data center are shown in the figure;

[0036] Figure 7 The device unit capacity utilization rate under normal operation in each scenario is shown in the figure. DETAILED DESCRIPTION

[0037] The present application will be described in detail below in combination with the drawings and specific embodiments. The present embodiment is implemented on the basis of the technical solution of the present application, and gives a detailed implementation manner and specific operation process, but the protection scope of the present application is not limited to the following embodiments.

[0038] With the wide application of technologies such as Internet of Things, cloud computing and artificial intelligence, the scale and application range of data centers are expanding. Data centers have prominent energy consumption, centralized distribution and easy control, and integrate power, heat and cold energy consumption and conversion. They can rely on cloud interconnection and data center network technology to realize spatiotemporal regulation of data load, adjust the spatiotemporal distribution of data center energy consumption, change the multi-energy load curve of post-disaster energy stations, relieve the energy supply pressure of energy stations in heavy disaster conditions, and provide new optimization space for the flexibility of energy stations. The response characteristics and waste heat recovery mechanism of data centers give them the potential to improve the flexibility of energy stations after an earthquake disaster, which is reflected in two aspects: first, in actual engineering, the seismic fortification intensity of data centers is different from that of energy stations. When the seismic intensity is the fortification intensity of energy stations, the data center should still be able to maintain normal operation and can fully play its role in demand response after a disaster; second, the computing equipment of data centers has a high degree of redundancy. Even if it suffers some degree of physical damage, it can still effectively maintain most of its computing functions. This inherent flexibility enables data centers to respond and undertake demand regulation tasks after an earthquake disaster, relieving the energy supply tension faced by energy stations after a disaster. Therefore, fully tapping the flexibility value of data center energy consumption and considering the coordination between data centers and energy stations after a disaster has important theoretical value and practical significance for improving the flexibility and economy of energy station planning schemes in response to earthquake disasters.

[0039] Based on the above considerations of data centers, the present application provides an energy station flexibility improvement planning method considering post-earthquake data center response, as shown in Figure 1 The method comprises the following steps:

[0040] S1, obtain energy station preset parameters and establish a multi-energy station failure model under earthquake disasters. Specifically, the energy station preset parameters include the energy station fortification earthquake intensity, the energy station candidate device type, the device earthquake vulnerability function, etc.

[0041] In this embodiment, different damage states of energy equipment under earthquake disasters are defined as different degrees of quantity loss, and on this basis, the corresponding damaged quantity expectation, i.e. the vulnerability model, can be obtained based on the earthquake vulnerability function of energy equipment. The vulnerability model is specifically embodied as the quantity loss of energy equipment under earthquake disasters, as shown in formula (1) and formula (2):

[0042]

[0043] In the formula: ξ fail represents the comprehensive loss coefficient of the quantity of energy equipment under earthquake disasters; θ k represents the quantity loss coefficient corresponding to the k-th damaged state of the energy equipment; v and V represent the index and set of energy equipment types, respectively; n v represents the quantity of energy equipment before the earthquake disaster; Δn vdenotes the number of damaged energy equipment after earthquake disaster; k max is the most serious damage state number.

[0044] The fault model of multi-energy station under earthquake disaster is shown as follows:

[0045]

[0046] In the formula: is the set of energy station fault states; denotes the set of damaged equipment type combinations u; denotes the set of damaged equipment numbers under different states; is the set of probabilities of different fault states; denotes the probability of the zth fault state; denotes the set of damaged equipment type combinations u under the zth fault state; denotes the probability of the ith equipment being in the most serious damage state under earthquake disaster; o denotes the order of the energy station fault set; u is the combination of equipment fault types under the order o of fault state, u is a subset of set V; Card(u) denotes the number of damaged equipment types, when o = 1, it means that one energy equipment is damaged.

[0047] S2, taking into account the dynamic changes of temperature inside the data center, a data center waste heat recovery model is constructed, the spatiotemporal adjustable characteristics of data load are utilized to coordinate energy flow and data flow, the spatiotemporal distribution of multi-energy load of post-earthquake energy station is reshaped, and the supply-demand balance of multi-energy station is changed.

[0048] (1) Data center energy consumption model

[0049] The multi-energy demand inside the data center is the electricity and cooling demand in the process of server processing data load, and the data load balance equation is shown in formula (3) to formula (5).

[0050]

[0051] In the formula: μ t is the arrival rate of data load of the data center; μ s,t is the data load service rate of the server running in the working condition s; X s,on,t is the number of servers running in the working condition s; t and T are the time period number and the corresponding set in the scheduling period respectively; s and Ω s are the server running working condition number and the corresponding set respectively; X on,t is the number of servers started in the time period t; X on and are the minimum value and the maximum value of the number of started servers respectively.

[0052] The data center energy consumption mainly includes information technology equipment energy consumption and refrigeration equipment energy consumption. Among them, the power consumed by the data center is mainly the power consumption of the server. Based on the dynamic voltage frequency adjustment technology, the server power consumption model can be constructed by using the utilization rate model, as shown in formula (6)-(8).

[0053]

[0054] In the formula: is the power consumption of the data center in the process of processing data load; and are the static power consumption and dynamic power consumption of the data center in the process of processing data load, respectively. e,sv,t is the static power consumption of a single server; e is the dynamic power consumption coefficient of a single server; s is the working frequency of the server working in the working condition s.

[0055] The power utilization efficiency is an important indicator to measure the energy efficiency of the data center, which means the ratio of the total power consumption of the data center to the power consumption of the information technology equipment, as shown in formula (9).

[0056]

[0057] In the formula: δ PUE is the PUE value of the data center; is the power consumption of the refrigeration equipment of the data center.

[0058] Based on the internal thermal steady state balance of the data center, it can be considered that the heat power generated by the data center server in the process of processing data load is equal to the refrigeration power of the refrigeration equipment, as shown in formula (10) and formula (11).

[0059]

[0060] In the formula: P c,DC,t is the refrigeration power of the data center refrigeration equipment; η c,DC is the working efficiency of the data center refrigeration equipment; P h,DC,t is the heat power generated by the data center in the process of processing data load.

[0061] Based on the above relationship between the total data load and the data center energy consumption, the data center energy consumption model is constructed, as shown in formula (3)-(11).

[0062] (2) Data center waste heat recovery model

[0063] The energy taste of the data center waste heat can be improved, and the improved energy taste can be converted and stored by the energy equipment in the energy station to meet the multi-energy load demand of the energy station. The consumed electric energy for improving the energy taste of the data center waste heat is shown in formula (12) and formula (13).

[0064]

[0065] In the formula, the consumed electric energy for improving the energy taste of the data center waste heat is shown in formula (12) and formula (13). The consumed thermal energy for improving the energy taste of the data center waste heat is shown in formula (12) and formula (13). DC The data center waste heat recovery coefficient is shown in formula (12) and formula (13). The data center waste heat recovery power is shown in formula (12) and formula (13).

[0066] Based on the data center waste heat recovery model, the first-order linear homogeneous differential equation based on the equivalent thermal parameter model is used to describe the dynamic change process of the temperature in the data center, as shown in formula (14). Based on the assumption of the thermal steady-state balance in the data center, the linear expression of formula (14) can be obtained, as shown in formula (15).

[0067]

[0068] In the formula, R and S are the heat exchange coefficient and the heat exchange area of the data center respectively. DC DC In the formula, R and S are the heat exchange coefficient and the heat exchange area of the data center respectively. In the formula, T and T are the external environment temperature and the internal temperature of the data center respectively. In the formula, P is the input cold power of the data center. air air In the formula, ρ and C are the density and the heat capacity of the air respectively. DC In the formula, V is the volume of the data center.

[0069] (3) Data load time-space regulation model

[0070] According to different real-time requirements, the data load can be divided into delay-sensitive data load with high real-time requirement and delay-tolerant data load with low real-time requirement. Correspondingly, the data load time-space regulation model includes a time balance model of the delay-tolerant data load and a space balance model of the delay-sensitive data load.

[0071] In the formula, the time balance model of the delay-tolerant data load is shown in formula (16)-(19).

[0072]

[0073] In the formula, Δt is the time period length, n is the number of the data center, and t is the time period. DC In the formula, t is the time period, and n is the number of the data center.​​​​​DC Latency-tolerant data loads in a data center; and They are the nth time intervals in time period t. DC A data center with unprocessed / processed latency-tolerant data load.

[0074] The spatial balancing model for latency-sensitive data loads is shown in equations (20) to (21).

[0075]

[0076] In the formula: For the nth DC The latency-sensitive data load processed by a data center during time period t; For the nth DC Latency-sensitive data loads local to a data center; For the nth DC Latency-sensitive data loads being transferred between data centers; and These represent the upper and lower limits for latency-sensitive data load transmission in data centers.

[0077] The response time of latency-sensitive data load interaction mainly includes average transmission delay and average waiting delay. In a regional integrated energy system, the average transmission delay of latency-sensitive data load can be regarded as a constant, and the average waiting delay can be described based on the M / M / 1 queuing theory, as shown in equations (22) to (23).

[0078]

[0079] In the formula: For the nth DC Average queuing latency for each data center; For the nth DC Average transmission latency of data centers; T tol This represents the upper limit of latency tolerance for the data center.

[0080] S3. Taking the minimum annual comprehensive cost as the objective function, and considering constraints such as the energy stations, data centers, inter-station energy connection pipelines, and the post-earthquake energy station load shedding limit, construct an energy station elastic upgrade planning model that takes into account the post-earthquake data center response.

[0081] (1) Objective function

[0082] The energy station resilience enhancement planning model for earthquake disaster response under data center access takes the minimum annual comprehensive cost as the objective function, including annual investment cost and annual operating cost, as shown in equation (24).

[0083] minC total =Cinv +C opr (twenty four)

[0084] In the formula: C total Annual comprehensive cost; C inv Annual investment cost; C opr This refers to the annual operating cost.

[0085] The annual investment cost includes the investment cost of equipment for multiple energy stations and the investment cost of interconnection pipelines between energy stations, as shown in Equation (25).

[0086]

[0087] In the formula: r and l are the discount rate and investment period, respectively; v and V are the numbers and sets of energy equipment, respectively; n ES and Ω ES These are the energy station's number and set, respectively; α and Ω. α These are the numbering and grouping of energy interconnection pipelines; m and Ω. m These are the energy type's number and set, respectively; Let v be the unit price of the energy equipment. For the nth ES The number of v-th type energy equipment invested and constructed in each energy station; The construction cost of the m-th type of energy connection pipeline; This refers to the construction status of energy interconnection pipelines.

[0088] Annual operating costs mainly include the purchase costs of various types of energy in each region during normal operation, as shown in equation (26).

[0089]

[0090] Where: m and Ω m These are the energy purchase number and set, respectively; d and Ω. d These are the typical day numbers and their corresponding sets; φ d U represents the number of days of d typical days in a year; m Let m be the purchase price of the m-th energy source; For the nth ES The m-th type of energy power purchased by the energy station.

[0091] (2) Constraints

[0092] 1) Load shedding limits for multiple energy stations after an earthquake disaster

[0093] The upper limit of the total load shedding of multiple types of energy at interconnected multi-energy stations after an earthquake disaster is used as the elasticity index, as shown in Equation (27).

[0094]

[0095] wherein: is the nth ES cut load of the mth sei energy of the energy station after the earthquake disaster; ε GB is the upper limit of the multi-energy cut load of the multi-energy station after the earthquake disaster.

[0096] 2) Energy equipment operation characteristic constraints

[0097] According to the number of different types of input and output energy, energy equipment can be divided into single-input single-output energy equipment, single-input multi-output energy equipment, and energy storage equipment.

[0098] Among them, as single-input single-output energy equipment, the operation characteristics of gas boilers, electric boilers, absorption refrigerators and electric refrigerators are shown in equations (28)-(35).

[0099]

[0100] wherein: η GB and are the output thermal power, input gas power, energy conversion coefficient and upper limit of gas power input of the gas boiler, respectively; η EB and are the output thermal power, input electric power, energy conversion coefficient and upper limit of electric power input of the electric boiler, respectively; η AC and are the output cooling power, input thermal power, energy conversion coefficient and upper limit of thermal power input of the absorption refrigerator, respectively; η EC and are the output cooling power, input electric power, energy conversion coefficient and upper limit of electric power input of the electric refrigerator, respectively.

[0101] Cogeneration units, as a single-input multi-output energy equipment, can convert natural gas into electric energy and thermal energy at the same time during normal operation, and their operation characteristics are shown in equations (36)-(38).

[0102]

[0103] wherein: and are the output electric power, output thermal power and input natural gas power of the cogeneration unit, respectively; η CHP and R he are the energy conversion coefficient and the thermal-to-electric ratio of the cogeneration unit, respectively; is the upper limit of the input gas power of the cogeneration unit.

[0104] Energy storage devices can charge and release energy according to the needs of the system, which can reshape the load curve and play a role in peak shaving and valley filling. The overall external operating characteristics of electrical energy storage and thermal energy storage are shown in equations (39) to (48).

[0105]

[0106] In the formula: and These represent the energy stored in electrical energy storage and thermal energy storage during time period t, respectively. and These represent the charging and discharging power of the energy storage during time period t; and These represent the charging and discharging efficiencies of electrical energy storage. and These are the charging and discharging flags for electrical energy storage and thermal energy storage during time period t, respectively. and These are the upper and lower limits of the charging and discharging power of the energy storage, respectively. and E BES These represent the upper and lower limits of the energy stored in electrical energy storage. and These refer to the energy stored during the initial and final stages of electrical energy storage, respectively. and These represent the charging and discharging power of thermal energy storage during time period t; and These represent the charging and discharging efficiencies of electrical energy storage. and These are the upper and lower limits of the charging and discharging power of thermal energy storage, respectively. and E HES These represent the upper and lower limits of the energy stored in thermal energy storage. and These represent the energy stored during the initial and final stages of thermal energy storage, respectively.

[0107] 3) Energy purchase constraints for energy stations

[0108] The maximum energy purchase of an energy station is limited by the external energy infrastructure of the energy station, as shown in equations (49) to (50).

[0109]

[0110] In the formula: and Purchase electricity for energy stations The upper and lower limits; and Purchase gas power for energy stations respectively The upper and lower limits.

[0111] 4) Energy interconnection pipeline transmission capacity constraints

[0112] Multi-energy stations can improve the utilization of energy equipment and realize the cross-regional cascade utilization of energy based on energy interconnection pipelines. The transmission capacity constraints of energy interconnection pipelines are shown in equation (51).

[0113]

[0114] In the formula: and P α,m are the upper and lower limits of the transmission power P α,m,t of the mth energy interconnection pipeline in the αth group of energy interconnection pipelines.

[0115] 5) Power balance constraints

[0116] The space-time adjustable characteristics and waste heat recovery characteristics of data centers can reshape the multi-energy load curve of energy stations, affecting the power balance equation of interconnected multi-energy stations based on the energy bus model, as shown in equations (52)-(55). The internal thermal power balance equation of the data center is shown in equation (17).

[0117]

[0118] In the formula: and are the purchased energy power of the energy station; and are the charging and discharging power of the electric energy storage; and are the electric power, gas power and thermal power of the combined heat and power unit; and are the electric power and cold power of the electric refrigerator; and are the electric power and thermal power of the electric boiler; and are the electric power, thermal power of waste heat recovery and input cold power consumed by the data center waste heat recovery; and are the gas power and thermal power of the gas boiler; and are the charging and discharging thermal power of the thermal energy storage; and are the thermal power and cold power of the absorption refrigerator; and are the electric, gas, thermal and cold loads of the nth ES energy station; and are the electric, gas, thermal and cold transmission power of the energy interconnection pipeline of the nth ES energy station.

[0119] 6) Temperature constraints inside the data center

[0120] To ensure the normal operation of the data center, it is necessary to limit the upper and lower limits of the internal temperature and the rate of change of the data center, as shown in equations (56) to (57).

[0121]

[0122] In the formula: and T in,DC The internal temperature T of the data center t in,DC The upper and lower limits; τ is the upper limit of the rate of temperature change inside the data center.

[0123] S4. Solve the energy station flexible upgrade planning model to obtain the selection and capacity setting scheme for energy equipment in multiple energy stations, as well as the planning scheme for energy interconnection pipelines between energy stations. In a specific implementation, the YALMP toolkit and Gurobi solver are used to solve the proposed planning model.

[0124] To verify the effectiveness of the proposed energy station resilience enhancement planning method for earthquake disaster response under data center access, an improved actual case system is used as an example. This example includes 3 energy stations and 3 data centers, with a distance of 5km between the energy stations.

[0125] Multi-energy load curves of different energy stations, such as Figure 2 As shown in Table 1, the specific parameters of the alternative equipment for the multi-energy station are as follows. The investment costs for the interconnecting pipelines are RMB 2 million / km (power line), RMB 1.5 million / km (gas pipeline), and RMB 1 million / km (heat and cold pipe). The capacity of each interconnecting pipeline is set at 5MW.

[0126] Table 1 Parameters of Alternative Energy Equipment for Energy Stations

[0127]

[0128] Time-series data loads of each data center, such as Figure 3 As shown in Table 2, the parameters of the data center are as follows. For latency-sensitive data loads, the maximum latency is set to 1 second; for latency-tolerant data loads, they must be processed within 24 hours; the PUE value of the data center is 1.5.

[0129] Table 2 Detailed parameters of the data center

[0130]

[0131] In this embodiment, the remaining parameters are set as follows: the planning period and the service life of the candidate energy equipment are both 15 years, and the discount rate is 5% per year. The energy purchase price of each region is the same, among which the electricity purchase price follows the time-of-use electricity price, i.e. 0.34 yuan / kWh (1:00-6:00, 22:00-24:00), 0.62 yuan / kWh (6:00-9:00, 12:00-13:00, 16:00-19:00) and 1.09 yuan / kWh (9:00-12:00, 13:00-16:00, 19:00-22:00); the gas price is stable at 2.81 yuan / m3; the total heat value of the gas is 41.04 MJ / m3 3 .

[0132] In this embodiment, in order to illustrate the influence of the post-earthquake response of the data center on the flexibility of the planning scheme of the energy station, four different scenarios are set as shown in Table 3.

[0133] Table 3 Planning scenarios of the energy station for responding to earthquake disasters

[0134]

[0135] The planning costs of the energy station under different planning scenarios are shown in Table 4. As can be seen from Table 4, scenario 2 considers the waste heat recovery characteristics of the data center on the basis of scenario 1, the waste heat generated by the data center is used as the heat source input of the post-disaster energy station, and after being converted and stored by the energy equipment, it can meet the multi-energy load demand, thereby reducing the disaster recovery capacity of the energy equipment of the energy station, so the sum of the annual equipment investment costs of the multi-energy station in scenario 2 is reduced by 7.23 million yuan, with a decrease of 12.74%. It should be pointed out that the annual operating cost of scenario 2 is increased by 0.37 million yuan compared with scenario 1, because scenario 1 configures energy equipment with more types and larger capacity to respond to earthquake disasters, which has higher flexibility and higher energy cascade utilization level during normal operation. Scenario 3 considers the time and space adjustable characteristics of the data center on the basis of scenario 1, compared with scenario 1, the data center can use the disaster recovery capacity of the energy equipment to the maximum extent during normal operation, thereby reducing the annual operating cost of the energy station, so the sum of the annual operating costs of the multi-energy station in scenario 3 is reduced by 5.78 million yuan, with a decrease of 1.72%, and the annual comprehensive cost is decreased by 1.39%. Scenario 4 considers both the waste heat recovery characteristics and the time and space adjustable characteristics of the data center, and the annual comprehensive cost is finally reduced to 38.33 million yuan, which is 16.30% lower than the annual equipment investment cost of scenario 1, and the annual comprehensive cost is decreased by 2.93%.

[0136] Table 4 Planning costs of the multi-energy station under different scenarios

[0137]

[0138] Table 5 shows the detailed planning scheme of energy equipment under different planning scenarios. Since scenario 2 considers the waste heat recovery characteristics of the data center, the energy station heat load demand can be directly met after the energy of the data center waste heat is improved after the earthquake disaster, so compared with scenario 1, the total capacity of the gas boiler and the electric boiler of the energy station heat production equipment in scenario 2 is reduced by 8MW and 4MW respectively. In addition, the waste heat generated by the data center can be flexibly met by the energy demand of the post-disaster energy station after being converted and stored by the absorption chiller, heat storage and other equipment, so the total capacity of the absorption chiller in scenario 2 is increased by 8MW, and the total capacity of the electric chiller is reduced by 8MW. Scenario 3 considers the time and space adjustable characteristics of data center energy consumption based on scenario 1. During normal operation, the time and space adjustment of data center based on data load can improve the disaster recovery capacity utilization rate of energy equipment, so the configuration of energy storage equipment in scenario 3 is more balanced. Scenario 4 can be regarded as a combination of scenario 2 and scenario 3. Compared with scenario 1, the capacity of gas boiler, electric boiler, electric chiller, combined heat and power unit, electric energy storage and heat storage in the planning scheme is reduced by 8MW, 4MW, 8MW, 8MW, 2MW / 4MWh and 4MW / 8MWh respectively, and the capacity of absorption chiller is increased by 8MW.

[0139] Table 5 Comparison of multi-energy station planning schemes under different scenarios

[0140]

[0141]

[0142] Figure 4 Table 5 shows the post-disaster allocation scheme of delay-sensitive data load under different planning scenarios. Among them, scenario 2 does not consider the time and space adjustment of data load, so Figure 4 (a) The allocation scheme of delay-sensitive data load is consistent with scenario 2, which can be regarded as a comparison scenario of scenario 3 and scenario 4. Figure 4 (b) is the allocation scheme of delay-sensitive data load in scenario 3. Since scenario 3 considers the spatial adjustment of delay-sensitive data load based on scenario 2, each data center can change the spatial distribution of multi-energy load after the earthquake disaster by adjusting the spatial allocation scheme of delay-sensitive data load, and reduce the load reduction of multi-energy station. Scenario 4 considers the time and space adjustable characteristics and waste heat recovery characteristics of data center energy consumption, and can adjust the time and space allocation scheme of data load of data center according to the time sequence difference of energy consumption of each energy station after the earthquake disaster, and reduce the disaster recovery capacity of energy station heating equipment as an emergency heat source for each energy station.

[0143] Figure 5Fig. 2 shows the post-disaster allocation of delay-tolerant data load of the second data center under different scenarios. In scenario 2, the spatiotemporal adjustability of the data load is not considered, and the allocation scheme of the delay-tolerant data load of the second data center is consistent with that of scenario 1, which can be used as a comparative scenario for scenario 3 and scenario 4. In scenario 3, since the refrigeration equipment in the energy station is mainly driven by heat, the delay-tolerant data load of the second data center at 22:00 is transferred to other time points for processing to avoid the peak heat demand of the second energy station. In scenario 4, both the spatiotemporal adjustability of the data center and the waste heat recovery feature are considered, so the delay-tolerant data load of the second data center at 21:00 is transferred to 22:00 for processing and waste heat recovery to meet the peak heat demand of the second energy station.

[0144] Figure 6 Fig. 3 shows the comparison of post-disaster heat power balance and internal temperature change of the second data center under different scenarios. Compared with scenario 2 and scenario 4, scenario 3 does not consider the waste heat recovery feature of the data center, so the cold power required by the data center during processing of the data load is provided by the energy station, as shown in (b) of Fig. 3. Figure 5 (b) of Fig. 3. In scenario 4, the spatiotemporal adjustability of the data center is considered based on scenario 2, and the heat generation power of the server and the waste heat recovery power of the data center in scenario 4 are both greater than those in scenario 2. This is because the heat load of the second energy station is greater than that of the first and third energy stations, and the first and third data centers transfer delay-sensitive data load to the second data center for processing, thereby increasing the waste heat power and reducing the post-disaster heat load reduction of the second energy station.

[0145] Figure 7 Fig. 4 shows the unit capacity utilization rate of the energy conversion equipment of the multiple energy stations under different scenarios, reflecting the redundant capacity of the equipment configured by the energy station to respond to the earthquake disaster during normal operation. As can be seen from Fig. 4, Figure 7 it can be seen that since scenario 2 and scenario 4 consider the waste heat recovery feature of the post-disaster data center, the equipment disaster backup capacity of the energy station to respond to the earthquake disaster is significantly reduced, so the unit capacity utilization rate of the energy equipment in scenario 2 and scenario 4 is significantly higher than that in scenario 1 and scenario 3.

[0146] Embodiment 2

[0147] The embodiment provides an energy station resilience improvement planning system considering post-disaster data center response, including one or more processors, a memory, and one or more programs stored in the memory, the one or more programs including instructions for executing the energy station resilience improvement planning method considering post-disaster data center response as described in embodiment 1.

[0148] Those skilled in the art will appreciate that embodiments of the present application can be readily used as software, hardware, or a combination of software and hardware. In a software embodiment, the methods can be tangibly embodied in a machine-readable storage medium having stored thereon instructions that can be used to program a computer to perform any of the methods. The software implementation can be initialized by loading and executing a set of instructions arranged to perform one of the methods into the computer's memory. Alternatively, hard-wired circuitry can be used in place of, or in combination with, software instructions. Thus, the

[0149] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams.

[0150] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams.

[0151] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams.

[0152] While preferred embodiments of the application have been described, additional variations and modifications can be made to these embodiments by those skilled in the art once they learn of the basic inventive concepts. Therefore, the appended claims are intended to cover all such modifications and variations as fall within the true scope of the present application.

[0153] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims, the application can be practiced otherwise than as specifically described herein.

[0154] The preferred embodiments of the application have been described above. It should be understood that various modifications can be made within the scope of the present concepts without departing from the spirit of the application. Therefore, it is intended that such modifications are to be included within the scope of the claims which are to be interpreted in the broadest sense allowable. CLAIM

Claims

1. A method for planning the resilient upgrade of energy stations considering the post-earthquake response of data centers, characterized in that, Includes the following steps: Obtain preset parameters for energy stations and establish a multi-energy station fault model under earthquake disasters; Based on constraints including the upper limit of the load shedding of the energy station after the earthquake and the internal temperature constraint of the data center, an energy station elastic upgrade planning model considering the response characteristics of the data center after the earthquake is constructed. The data center response characteristics include the data center energy consumption characteristics, waste heat recovery characteristics and the spatiotemporal adjustable characteristics of energy consumption. The energy station flexible upgrade planning model is solved to obtain the selection and capacity setting scheme of energy equipment for multiple energy stations, as well as the planning scheme of energy interconnection pipelines between energy stations; The preset parameters of the energy station include the seismic intensity of the energy station, the types of equipment to be selected for the energy station, and the seismic vulnerability function of the equipment. The energy station fault model is represented as follows: In the formula: This is a set of fault states for the energy station. Indicates the combination of damaged equipment types A set; This represents the set of equipment damage quantities under different conditions; It is the set of probabilities for different fault states; Indicates the first The probability of a certain fault state; Indicates the first Combination of damaged equipment types under various fault conditions A set; Indicates the first The probability that a certain type of equipment will be in the most severely damaged state under an earthquake disaster; Indicates the order of the fault set of the energy station; Fault state order The following combinations of equipment failure types This represents a set of energy equipment types. For set A subset of; Indicates the type and quantity of damaged equipment; The upper limit constraint for load shedding at the post-earthquake energy station is: In the formula: For the first time after the earthquake disaster The first energy station The load shedding capacity of this type of energy; This is the upper limit for multi-energy load shedding at multiple energy stations after an earthquake disaster; and These are the energy type's number and set, respectively; and These are the energy station's number and set, respectively.

2. The energy station resilient upgrade planning method considering post-earthquake data center response as described in claim 1, characterized in that, When constructing the energy station elastic upgrade planning model, the dynamic changes in internal temperature of the data center and the characteristics of waste heat recovery of the data center are taken into account. The spatiotemporal adjustability of data load is used to coordinate energy flow and data flow, reshape the spatiotemporal distribution of multi-energy loads of the energy station after the earthquake, and change the supply and demand balance of multiple energy stations.

3. The energy station resilient upgrade planning method considering post-earthquake data center response as described in claim 1, characterized in that, The waste heat recovery characteristics are characterized by a data center waste heat recovery model, which is as follows: In the formula: The electrical energy consumed to improve the quality of waste heat energy in data centers; The heat energy consumed to improve the quality of waste heat energy in data centers; The waste heat recovery coefficient for data centers; For data center waste heat recovery power; and These are the heat transfer coefficient and heat transfer area of ​​the data center, respectively. and These refer to the external ambient temperature and the internal temperature of the data center, respectively. Provide cooling power to the data center; and These are the density and heat capacity of air, respectively. For the volume of the data center; and These are the time period numbers and corresponding sets within the scheduling period; The heat power generated during the data load processing in the data center.

4. The energy station resilient upgrade planning method considering post-earthquake data center response as described in claim 1, characterized in that, The time-space adjustable energy consumption characteristics are characterized by a time-balanced model for delay-tolerant data loads and a space-balanced model for delay-sensitive data loads.

5. The energy station resilient upgrade planning method considering post-earthquake data center response as described in claim 1, characterized in that, The energy consumption characteristics of the data center include the energy consumption of information technology equipment and the energy consumption of cooling equipment.

6. The energy station resilient upgrade planning method considering post-earthquake data center response as described in claim 1, characterized in that, The energy station resilient upgrade planning model takes the minimum annual comprehensive cost as its objective function. The constraints of the energy station resilient upgrade planning model also include constraints on the operating characteristics of energy equipment, energy station energy purchase constraints, energy connection pipeline transmission capacity constraints, and power balance constraints.

7. A power station resilience upgrade planning system considering post-earthquake data center response, comprising one or more processors, a memory, and one or more programs stored in the memory, said one or more programs including instructions for executing the power station resilience upgrade planning method considering post-earthquake data center response as described in any one of claims 1-6.

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