Energy station flexibility improvement planning method and system under multiple uncertainties

Abstract

The present invention relates to a method and system for planning energy station resilience under multiple uncertainties, the method comprising the following steps: considering multiple uncertain factors of the energy station disaster process, constructing the probability distribution of each uncertainty factor, the multiple uncertainty factors including earthquake intensity deviation, equipment damage state capacity deviation, energy equipment earthquake vulnerability deviation, post-disaster multi-energy load prediction error, and post-disaster data load prediction error; based on the probability distribution of each uncertainty factor, constructing the risk constraint of the energy station post-disaster load shedding condition using the conditional risk value theory; constructing an energy station resilience improvement planning model for coping with earthquake disasters under multiple uncertainties; solving the energy station resilience improvement planning model to obtain a multi-energy station planning scheme for coping with earthquake disasters. Compared with the prior art, the present invention has the advantages of being able to effectively avoid the potential risk of post-disaster load shedding of energy stations and improving the resilience of planning schemes to cope with earthquake disasters.

Description

Technical Field

[0001] The present invention relates to the field of energy station resilience improvement planning for coping with earthquake disasters, and in particular to an energy station resilience improvement planning method and system under multiple uncertainties. Background Art

[0002] With the transformation of the global energy structure and the continuous growth of energy demand, the resilience of energy stations is particularly important. Energy station resilience improvement planning aims to ensure the stability, security and sustainability of energy supply to cope with various uncertainties and challenges.

[0003] As a natural disaster, earthquakes pose serious challenges to the normal operation of energy stations and energy supply systems due to their suddenness and destructiveness. The intensity of earthquake motion is greatly affected by the dynamic evolution of multiple factors such as earthquake wave amplitude, frequency, duration and phase parameters in the space-time dimension. At the same time, the seismic vulnerability of energy equipment is related to factors such as the environment in which the equipment is located, the degree of aging and the degree of material variation. These dynamic spatiotemporal information carried by the intensity of earthquake motion and the seismic vulnerability of energy equipment are inevitably transmitted to the vulnerability model of energy equipment and the energy station planning model for earthquake disasters. The planning scheme faces the risk of failure. In addition, affected by earthquake disasters, the irregular disconnection of various loads after the disaster, various new loads caused by various emergency repair actions and measures will reshape the energy characteristics and spatiotemporal distribution of multi-energy loads after the disaster. The energy station has a serious problem of "source-load" mismatch after the disaster and faces the risk of load shedding after the disaster.

[0004] However, the prior art does not disclose relevant methods to effectively avoid the potential risks of various uncertainties caused by earthquake disasters on energy station planning. Therefore, how to consider various uncertainties in the process of energy station disasters and realize reliable energy station resilience improvement planning is a problem that needs to be solved in this field. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide an energy station resilience planning method and system that can effectively avoid the potential risks of post-disaster load shedding in energy stations and improve the resilience of planning schemes to cope with multiple uncertainties of earthquake disasters.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A method for planning energy station resilience improvement under multiple uncertainties includes the following steps:

[0008] Considering multiple uncertain factors in the disaster process of the energy station, the probability distribution of each uncertainty factor is constructed, wherein the multiple uncertain factors include the deviation of earthquake intensity, the deviation of equipment damage state capacity, the deviation of energy equipment seismic vulnerability, the error of post-disaster multi-energy load prediction, and the error of post-disaster data load prediction;

[0009] Based on the probability distribution of each uncertainty factor, the conditional risk value theory is used to construct the risk constraints of the load shedding conditions after the disaster of the energy station;

[0010] Based on the risk constraints of the post-disaster load shedding conditions of the energy station, a resilience improvement planning model for energy stations in response to earthquake disasters under multiple uncertainties is constructed;

[0011] The energy station resilience improvement planning model is solved to obtain a multi-energy station planning scheme for coping with earthquake disasters.

[0012] Furthermore, the probability distribution of the earthquake intensity deviation is:

[0013]

[0014] Where: and Represents the earthquake intensity parameter r PGA The expected and standard deviation of volatility, Δr PGA Indicates the deviation of earthquake intensity.

[0015] Furthermore, the probability distribution of the equipment damage state capability deviation is:

[0016]

[0017] Where: Represents the error of the energy equipment quantity loss coefficient under the kth damage state; are the expectation and standard deviation of the energy equipment quantity loss coefficient error, respectively.

[0018] Furthermore, the probability distribution of the energy equipment seismic vulnerability deviation is:

[0019]

[0020] Where: and They represent the median value and logarithmic standard deviation of the vulnerability function corresponding to the kth damage state after the energy equipment mutates; β var The coefficient of variation of seismic vulnerability of energy equipment, μ k and δ k They respectively represent the median value and logarithmic standard deviation of the vulnerability function corresponding to the kth damage state of energy equipment under earthquake disasters.

[0021] Furthermore, the probability distribution of the post-disaster multi-energy load forecast error is:

[0022]

[0023] Where: ΔL m represents the prediction error of the mth energy source after the disaster; and They represent the expectation and standard deviation of the prediction error of the m-th energy source respectively.

[0024] Furthermore, the probability distribution of the post-disaster data load prediction error is:

[0025]

[0026] Where: ΔWL represents the prediction error of post-disaster data load; μ ΔWL and σ ΔWL represents the mean and standard deviation of the data load prediction error.

[0027] Furthermore, when the conditional risk value theory is used to construct the risk constraints of the energy station's post-disaster load shedding conditions, the risk threshold and confidence level of the energy station's post-disaster load shedding conditions are adjusted according to the decision maker's preference for risk.

[0028] Furthermore, when constructing an energy station resilience improvement planning model based on the post-disaster load shedding conditional risk constraints of the energy station, multi-scenario technology is used to convert the post-disaster load shedding conditional risk constraints of the energy station into a linear combination of transformation functions, and the linearized conditional risk constraints are embedded in the energy station resilience improvement planning model.

[0029] Furthermore, the energy station resilience enhancement model for coping with earthquake disasters under multiple uncertainties takes the minimization of annual comprehensive costs including the expected value of post-disaster load shedding risk costs as the objective function, and the constructed constraints include energy station constraints, data center constraints and the energy station post-disaster load shedding risk constraints.

[0030] Furthermore, the energy station resilience improvement model for coping with earthquake disasters under the multiple uncertainties is solved based on the branch and bound method of the commercial solver Gurobi.

[0031] The present invention also provides an energy station resilience improvement planning system under multiple uncertainties, comprising one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the energy station resilience improvement planning method under multiple uncertainties as described above.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] (1) The present invention takes into account multiple uncertainty factors including earthquake intensity deviation, equipment damage state capacity deviation, energy equipment seismic vulnerability deviation, post-disaster multi-energy load prediction error and post-disaster data load prediction error, and constructs the post-disaster load shedding risk constraint of the energy station based on the probability distribution model of each uncertainty factor, which can cover more "tail risk" information and effectively avoid the post-disaster load shedding tail risk faced by the energy station through forward-looking planning, with high planning reliability.

[0034] (2) The present invention can adjust the risk threshold and confidence level of the post-disaster load shedding conditions of the energy station according to the decision maker's risk preference, thereby obtaining an energy station planning scheme that is more effective in responding to earthquake disasters under multiple uncertainties, with high flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the process of the present invention;

[0036] Figure 2 It is a schematic diagram of the damage state threshold, damage state, and damage state capacity of infrastructure under earthquake disasters;

[0037] Figure 3 It is a multi-energy load diagram of an energy station in an embodiment;

[0038] Figure 4 The data load diagram of the data center of the embodiment is shown;

[0039] Figure 5 To illustrate post-disaster load shedding and “tail risk” under different methods;

[0040] Figure 6 is the unit capacity utilization rate of the equipment during normal operation of the embodiments under different methods;

[0041] Figure 7 It is the planning cost and risk value of post-disaster load shedding conditions under different confidence levels. DETAILED DESCRIPTION

[0042] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0043] Example 1

[0044] This embodiment provides a method for planning energy station flexibility improvement under multiple uncertainties, such as Figure 1 As shown, the following steps are included:

[0045] S1. Consider multiple uncertain factors in the disaster process of the energy station, construct the probability distribution of each uncertainty factor, and realize the modeling of multiple planning uncertainty factors. In this embodiment, the multiple uncertain factors considered include earthquake intensity deviation, equipment damage state capacity deviation, energy equipment seismic vulnerability deviation, post-disaster multi-energy load prediction error, and post-disaster data load prediction error.

[0046] (1) Modeling of uncertainty in ground motion intensity parameters

[0047] The ground motion intensity parameters include parameters related to the structural properties and parameters unrelated to the structural properties. Among them, the magnitude of the peak ground acceleration r PGA It is independent of the structural properties and is often used to assess the vulnerability of infrastructure under earthquake disasters. PGA The relationship between the earthquake intensity and the ground motion intensity parameter r is shown in Table 1. PGA There is no obvious corresponding relationship between the size of the ground motion parameter r. PGA The uncertainty of fluctuation is shown in formula (1).

[0048]

[0049] Where: and Represents the earthquake intensity parameter r PGA The expected and standard deviation of volatility, Δr PGA Indicates the deviation of earthquake intensity.

[0050] Table 1r PGA Relationship with earthquake intensity

[0051]

[0052] (2) Uncertainty modeling of equipment damage state capability

[0053] The resilience assessment of infrastructure under earthquake disasters usually relies on probabilistic analysis methods. Among them, the establishment of the damage state threshold (DST) is of decisive significance for describing the probability distribution curve of infrastructure vulnerability. The definition of is crucial to the vulnerability assessment of infrastructure. The relationship between infrastructure damage state threshold, damage state, and damage state capacity under earthquake disasters is as follows: Figure 2As shown. The infrastructure damage state capacity is generally based on historical data statistics, experimental tests or expert judgment, and there are certain errors. In the energy station planning for earthquake disasters, the energy equipment damage state capacity is defined as the remaining number of equipment after the earthquake disaster. In order to simplify the model, this embodiment selects the normal distribution function to describe the volatility of the energy equipment damage state capacity under earthquake disasters, as shown in formula (2).

[0054]

[0055] Where: Represents the error of the energy equipment quantity loss coefficient under the kth damage state; are the expectation and standard deviation of the energy equipment quantity loss coefficient error, respectively.

[0056] (3) Uncertainty modeling of equipment seismic vulnerability

[0057] The seismic vulnerability of energy equipment represents the conditional probability of energy equipment experiencing or exceeding different damage states under a given seismic demand level. It is generally assumed to obey a log-normal distribution, as shown in formula (3). k and δ k They represent the median value and logarithmic standard deviation of the vulnerability function corresponding to the kth damage state of energy equipment under earthquake disasters, which are related to the physical properties of energy equipment. From a practical point of view, the seismic vulnerability of energy equipment is affected by the configuration environment, component aging, structural variation, etc. Equations (4) and (5) are used to describe the random variation of the seismic vulnerability of energy equipment under earthquake disasters.

[0058]

[0059] Where: k represents the index of the damage state of public infrastructure under earthquake disaster; λ k represents the probability of public infrastructure reaching the kth damage state; and They represent the median value and logarithmic standard deviation of the vulnerability function corresponding to the kth damage state after the energy equipment mutates; β var The coefficient of variation that represents the seismic vulnerability of energy equipment.

[0060] (4) Uncertainty modeling of multi-energy load and data load demand after earthquake disasters

[0061] After an earthquake disaster, the spatiotemporal distribution of multi-energy load and data load after the disaster shows different probability distribution characteristics, affected by various factors such as the urgency of user energy demand, random disconnection of multi-energy load, meteorological conditions, time, and additional energy consumption of emergency repair operations. Therefore, this embodiment selects the commonly used normal distribution to describe the uncertainty of the prediction error of multi-energy load and data load before and after the earthquake disaster, as shown in equations (6) and (7).

[0062]

[0063] Where: ΔL m represents the prediction error of the mth energy source after the disaster; and They represent the expectation and standard deviation of the prediction error of the αth energy source; ΔWL represents the prediction error of the post-disaster data load; μ ΔWL and σ ΔWL represents the expectation and standard deviation of the data load prediction error; e, g, h, c represent electricity, gas, heating, and cooling energy, respectively.

[0064] S2. Based on the probability distribution of each uncertainty factor, the conditional risk value theory is used to construct the risk constraints of the energy station's post-disaster load shedding conditions to avoid the post-disaster load shedding risks that the planning scheme may face.

[0065] The decision variable x represents the energy station planning scheme, y represents a random variable composed of uncertain factors, ρ(y) represents a random variable y The probability density function of , and the loss function f(x,y) represents the post-disaster load shedding of the energy station when the configuration scheme is x and the random variable is y. On this basis, we can establish a given confidence level β and conditional risk threshold q CVaR The risk constraint of the post-disaster load shedding condition of the energy station is shown in formula (8).

[0066]

[0067] S3. Based on the risk constraints of the post-disaster load shedding conditions of the energy station, a resilience improvement planning model for energy stations to cope with earthquake disasters under multiple uncertainties is constructed.

[0068] (1) Objective function

[0069] In this embodiment, the energy station resilience improvement planning model for coping with earthquake disasters under multiple uncertainties aims to minimize the annual comprehensive cost including the expected value of the post-disaster load shedding risk cost, where the specific calculation formula for the expected value of the post-disaster load shedding risk penalty cost is shown in formula (9).

[0070]

[0071] Where: cm represents the unit load shedding penalty cost of the mth energy source after an earthquake disaster; CVaR m represents the conditional risk value of the mth energy load shedding after an earthquake disaster, m and Ω m They are the number and set of energy types respectively.

[0072] (2) Constraints

[0073] In this embodiment, the constraints of the energy station resilience improvement planning model for earthquake disasters under multiple uncertainties mainly include energy station constraints, data center constraints, and post-disaster load shedding condition risk constraints. Among them, the energy station post-disaster load shedding condition risk constraints are first converted into a linear combination of transformation functions using multi-scenario technology, and then the linearized conditional risk constraints are embedded in the energy station resilience improvement planning model.

[0074] In order to embed the conditional risk constraints into the planning model to facilitate the solution of the planning model, this embodiment establishes a linearized post-disaster load shedding conditional risk constraint for the energy station based on multi-scenario technology, as shown in equations (10) to (13).

[0075]

[0076] Where: is the load shedding power of the mth type of energy in the energy station after the disaster; Δt is the duration of the time period; The nth day after the earthquake disaster ES The conditional risk value of the mth energy source load shedding of an energy station; Represents a collection of energy stations; The nth day after the earthquake disaster ES The risk value of shedding the mth energy source at an energy station; is a non-negative intermediate variable introduced in the linearization process; β is a given confidence level; s is the number of different scenarios; The nth day after the earthquake disaster ES The upper limit of risk value of the mth energy load shedding condition of an energy station.

[0077] S4. Solve the energy station resilience improvement planning model to obtain a multi-energy station planning scheme for coping with earthquake disasters, including a multi-energy station internal equipment selection and sizing scheme and a planning scheme for energy interconnection pipelines.

[0078] In this embodiment, the YALMIP toolkit and the Gurobi solver are called to solve the optimization problem and obtain a planning scheme for multiple energy stations.

[0079] The above method aims at the risk problem of heterogeneous energy load shedding in post-disaster energy stations caused by the above-mentioned multiple uncertainties and their coupling. It considers multiple uncertainty factors including earthquake intensity deviation, equipment damage state capacity deviation, energy equipment seismic vulnerability deviation, post-disaster multi-energy load prediction error and post-disaster data load prediction error, establishes risk constraints on post-disaster load shedding conditions for energy stations, and effectively avoids the potential risk of post-disaster load shedding of energy stations caused by uncertain parameters in the process of energy equipment selection and sizing. It has important theoretical value and practical significance for improving the resilience of energy station planning schemes to cope with earthquake disasters.

[0080] In order to verify the effectiveness of the energy station resilience improvement planning method for earthquake disasters under the above multiple uncertainties, an improved actual example system is taken as an example. The example includes 3 energy stations and 3 data centers, and the distance between the energy stations is 5 km.

[0081] The multi-energy load curves of different energy stations are as follows: Figure 3 The specific parameters of the optional equipment for the multi-energy station are shown in Table 2. The investment costs of the interconnected pipelines are 2 million yuan / km (power line), 1.5 million yuan / km (gas pipeline), and 1 million yuan / km (heat and cold pipes). The capacity of each interconnected pipeline is set to 5MW.

[0082] Table 2 Parameters of alternative energy equipment for energy stations

[0083]

[0084] The time series data load of each data center is as follows: Figure 4 The parameters of the data center are shown in Table 3. For delay-sensitive data loads, the maximum delay is set to 1 second; for delay-tolerant data loads, they must be processed within 24 hours; and the PUE value of the data center is 1.5.

[0085] Table 3 Data center detailed parameters

[0086]

[0087] In this embodiment, the remaining parameters are set as follows: the planning period and service life of the selected energy equipment are both 15 years, and the discount rate is 5% per year. The purchase price of energy in each region is the same, among which the purchase price follows the time-of-use electricity price, namely 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 calorific value of the gas is 41.04MJ / m 3; The penalty fee for the "tail risk" of heterogeneous energy load shedding is 7 yuan / kWh; the CVaR confidence level of heterogeneous energy load shedding is set to 85%; the upper limit of the risk of heterogeneous energy load shedding conditions is set to 3MWh; the prediction error of multi-energy load and data load after the earthquake disaster obeys a normal distribution with a mean of 0 and a variance of 0.025 times the basic value; the deviation of the seismic intensity parameter obeys a normal distribution with a mean of 0 and a variance of 0.1 times the basic value; the deviation of the coefficient of variation of the seismic vulnerability curve of energy equipment obeys a normal distribution with a mean of 0 and a variance of 0.15 times the basic value; the error of the quantity loss coefficient under each damage state of energy equipment obeys a normal distribution with a mean of 0 and a variance of 0.1 times the basic value.

[0088] In order to analyze in more detail the impact of the “tail risk” of post-disaster load shedding of energy stations on planning schemes under multiple uncertainties and verify the effectiveness of the proposed planning method, three different planning methods are set for comparison:

[0089] Method I: Ignoring the spatiotemporal adjustable characteristics of data center energy consumption and waste heat recovery characteristics, and ignoring the “tail risk” of load shedding after a disaster at an energy station, a random optimization method is used to obtain an energy station planning scheme for earthquake disasters under multiple uncertainties;

[0090] Method II: Considering the spatiotemporal adjustable characteristics of data center energy consumption and waste heat recovery characteristics, but without controlling the “tail risk” of post-disaster load shedding, random planning of energy stations to cope with earthquake disasters under multiple uncertainties is carried out;

[0091] Method III: Considering the participation of data centers in demand response after disasters, while controlling the “tail risk” of post-disaster load shedding, random planning of energy stations is carried out taking into account the risk of post-disaster load shedding.

[0092] Table 4 compares the impact of post-earthquake data center participation in demand response and control of post-disaster load shedding "tail risk" on planning costs. Method I does not consider the spatiotemporal adjustable characteristics of post-disaster data center energy consumption and waste heat recovery characteristics, and its annual comprehensive cost is the highest, further illustrating the superiority of data center participation in demand response after the disaster in improving the economic efficiency of energy station planning. Based on Method I, Method II considers post-disaster data center participation in demand response, and its annual equipment investment cost is reduced by 3.08%, annual operating cost is reduced by 1.88%, and annual comprehensive cost is reduced by 2.02%. Method III further controls the "tail risk" of post-disaster load shedding on the basis of Method II, and its annual comprehensive cost increases by 2.79 million yuan compared with Method II, among which the annual equipment investment cost increases by 11.51% and the annual operating cost decreases by 0.84%.

[0093] Table 4 Planning costs of energy stations under different planning methods

[0094]

[0095] Table 5 compares the detailed planning schemes of energy stations under different methods. Method II takes into account the participation of data centers in demand response after disasters on the basis of Method I, and the disaster recovery capacity of equipment is greatly reduced, indicating that under the influence of multiple uncertain factors, data centers can reshape the spatiotemporal distribution curve of multi-energy loads of energy stations after disasters based on the characteristics of adjustable spatiotemporal energy consumption and waste heat recovery, and reduce the disaster recovery capacity of equipment in energy stations to cope with multiple uncertain factors. Method III further controls the "tail risk" of post-disaster load shedding on the basis of Method II, and the disaster recovery capacity of the configured energy equipment is further increased. Since Method III controls the "tail risk" of post-disaster load shedding of different energy sources of each energy station separately, the disaster recovery capacity of energy equipment in different energy stations varies. Among them, Energy Station No. 2 has the largest demand for heating on typical days in winter and the largest demand for cooling on typical days in summer. Compared with Method II, in the planning scheme obtained by Method III, Energy Station No. 2 is equipped with 6 gas boilers, 1 electric boiler, and 6 electric refrigerators, with the most additional disaster recovery equipment; Energy Station No. 1 is equipped with 1 thermal energy storage, and Energy Station No. 3 is equipped with 4 electric refrigerators and 3 thermal energy storage.

[0096] Table 5 Energy station planning schemes under different planning methods

[0097]

[0098] Figure 5 The post-disaster load shedding of multiple energy stations and the “tail risk” of load shedding under different planning methods are compared. Figure 5 It can be seen that since Method I does not consider the spatiotemporal adjustable characteristics and waste heat recovery characteristics of data center energy consumption, although Method I configures more disaster recovery capacity of energy equipment than Method II, the post-disaster load shedding of energy stations in the planning scheme obtained by it still has no obvious advantage over Method II, and the load shedding of the planning scheme of Energy Station No. 3 obtained by it is significantly higher than that of Method II and Method III. Method II considers the participation of data centers in post-disaster demand response on the basis of Method III, which can effectively deal with the impact of multiple uncertain factors on the elastic performance of energy station planning schemes and improve the economy of energy station planning. However, Method II does not consider the control of the "tail risk" of post-disaster load shedding. Therefore, although the planning scheme obtained by Method II has a smaller load shedding than the planning scheme obtained by Method I in most cases, it faces the same potential risk of load shedding as Method I. In some extreme cases, the post-disaster load shedding "tail risk" faced by the planning scheme obtained by Method II is more severe than that of Method I, such as Figure 5(b) shows that although considering the participation of data centers in demand response after a disaster can reduce the disaster recovery capacity of energy station energy equipment and improve the economy of energy station planning, it will make the "tail risk" caused by uncertainty parameters more extreme. Method III further considers the control of the "tail risk" caused by uncertainty parameters on the basis of Method II. Therefore, the post-disaster load shedding and "tail risk" of the planning scheme obtained by Method III are significantly lower than those of Method I and Method II.

[0099] Figure 6 The unit capacity utilization of energy conversion equipment during normal operation of the planning schemes obtained by different methods is compared and analyzed. Figure 6 It can be seen that the unit capacity utilization rate of energy equipment in the planning scheme obtained by Method II is significantly higher than that of Method I and Method III. This is because the capacity of the cogeneration unit configured in Method II is the largest. During normal operation, it can fully cooperate with absorption chillers, thermal energy storage, electric energy storage and other equipment to improve the unit capacity utilization rate of energy station equipment and reduce the amount of electricity purchased by the superior power grid. This also explains why the operating cost of Method II in Table 4 is lower than that of Method I.

[0100] In order to analyze the impact of the risk of post-disaster load shedding of energy stations on the planning results, based on method III, the confidence levels were taken as 75%, 80%, 85%, 90%, and 95%, respectively, and the comparison results of the planning costs of multiple energy stations and the CVaR of post-disaster load shedding were obtained, as shown in the following figure: Figure 7 As shown. Figure 7 It can be seen that the CVaR of post-disaster load shedding of energy stations increases with the increase in confidence levels. This is because CVaR reflects the mean risk of the part that exceeds VaR at a given confidence level, and VaR essentially identifies the possible load shedding threshold of the post-disaster energy station at a given confidence level, that is, the corresponding quantile. When the confidence level increases, VaR rises accordingly, which means that the set load shedding risk threshold is more stringent, and the corresponding risk quantile position moves to the right, resulting in a corresponding narrowing of the tail risk interval included in the CVaR calculation. At the same time, the load shedding conditions covered by this part of the interval are more extreme, with a lower probability of occurrence but a greater potential impact. In terms of equipment configuration, the disaster recovery capacity of energy equipment needs to be increased to cope with this more extreme load shedding situation, so Figure 7 The annual investment cost also increases with the increase of confidence level.

[0101] Example 2

[0102] The present invention also provides an energy station resilience improvement planning system under multiple uncertainties, comprising one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the energy station resilience improvement planning method for responding to earthquake disasters under multiple uncertainties as described in Example 1.

[0103] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0104] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0107] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0108] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0109] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for planning energy station flexibility improvement under multiple uncertainties, characterized in that: The following steps are involved: Considering multiple uncertain factors in the disaster process of the energy station, the probability distribution of each uncertainty factor is constructed, wherein the multiple uncertain factors include the deviation of earthquake intensity, the deviation of equipment damage state capacity, the deviation of energy equipment seismic vulnerability, the error of post-disaster multi-energy load prediction, and the error of post-disaster data load prediction; Based on the probability distribution of each uncertainty factor, the conditional risk value theory is used to construct the risk constraints of the load shedding conditions after the disaster of the energy station; Based on the risk constraints of the post-disaster load shedding conditions of the energy station, a resilience improvement planning model for energy stations in response to earthquake disasters under multiple uncertainties is constructed; Solving the energy station resilience improvement planning model to obtain a multi-energy station planning scheme to cope with earthquake disasters; When constructing an energy station resilience improvement planning model based on the energy station post-disaster load shedding condition risk constraint, the energy station post-disaster load shedding condition risk constraint is converted into a linear combination of transformation functions using multi-scenario technology, and the linearized conditional risk constraint is embedded in the energy station resilience improvement planning model; The energy station resilience improvement planning model for coping with earthquake disasters under multiple uncertainties takes the minimization of the annual comprehensive cost including the expected value of the post-disaster load shedding risk cost as the objective function, and the constructed constraints include energy station constraints, data center constraints and the post-disaster load shedding risk constraints of the energy station.

2. The energy station flexibility improvement planning method under multiple uncertainties according to claim 1 is characterized in that: The probability distribution of the ground motion intensity deviation is: Where: and Represents the earthquake intensity parameter r PGA The expected and standard deviation of volatility, Δr PGA Indicates the deviation of earthquake intensity.

3. The method for planning energy station flexibility improvement under multiple uncertainties according to claim 1 is characterized in that: The probability distribution of the equipment damage state capability deviation is: Where: Δθ k Represents the error of the energy equipment quantity loss coefficient under the kth damage state; are the expectation and standard deviation of the energy equipment quantity loss coefficient error, respectively.

4. The energy station flexibility improvement planning method under multiple uncertainties according to claim 1 is characterized in that: The probability distribution of the energy equipment seismic vulnerability deviation is: Where: and They represent the median value and logarithmic standard deviation of the vulnerability function corresponding to the kth damage state after the energy equipment mutates; β var The coefficient of variation of seismic vulnerability of energy equipment, μ k and δ k They respectively represent the median value and logarithmic standard deviation of the vulnerability function corresponding to the kth damage state of energy equipment under earthquake disasters.

5. The energy station flexibility improvement planning method under multiple uncertainties according to claim 1 is characterized in that: The probability distribution of the post-disaster multi-energy load forecast error is: Where: ΔL m represents the prediction error of the mth energy source after the disaster; and They represent the expectation and standard deviation of the prediction error of the m-th energy source respectively.

6. The method for planning energy station flexibility improvement under multiple uncertainties according to claim 1, characterized in that: The probability distribution of the post-disaster data load prediction error is: Where: ΔWL represents the prediction error of post-disaster data load; μ ΔWL and σ ΔWL represents the expected and standard deviation of the data load prediction error.

7. The method for planning energy station flexibility improvement under multiple uncertainties according to claim 1, characterized in that: When constructing the risk constraints of energy station post-disaster load shedding conditions using the conditional risk value theory, the risk threshold and confidence level of the energy station post-disaster load shedding conditions are adjusted according to the decision maker's preference for risk.

8. An energy station flexibility improvement planning system under multiple uncertainties, characterized in that: It comprises one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the energy station resilience improvement planning method under multiple uncertainties as described in any one of claims 1-7.

Images