Electro-hydrogen energy system risk defense planning method based on self-adaptive time sequence polymerization

Abstract

The invention provides an electro-hydrogen energy system risk defense planning method based on adaptive time sequence aggregation, and relates to the technical field of electro-hydrogen energy system planing.The method comprises the steps that firstly, an adaptive time sequence aggregation algorithm is designed according to predicted values of renewable energy sources and load demands, and the variable dimension of operation simulation is reduced; secondly, introducing a conditional value-at-risk theory, and modeling renewable energy sources and electrical load fluctuation by adopting multivariate Gaussian distribution to quantify potential operation risks of the system; and finally, considering system investment and operation cost, and by taking annual total cost minimization as an optimization target, constructing an electro-hydrogen energy system optimization planning model based on self-adaptive time sequence aggregation. According to the risk defense planning method for the electro-hydrogen energy system based on the self-adaptive time sequence polymerization, the adjustment capacity of long / short-period hydrogen storage can be considered at the same time, and the potential operation risk and investment cost of the system are effectively balanced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric hydrogen energy system planning, and in particular to a risk defense planning method for an electric hydrogen energy system based on adaptive time series aggregation. Background Art

[0002] For large-scale renewable energy hydrogen production entities with a single interconnection point with the transmission grid, they can be regarded as a hybrid resource type and an emerging market entity. When the new power system develops into a high-proportion renewable energy penetration stage, the grid peak load redundancy is further reduced. The renewable energy hydrogen production system fully utilizes renewable energy to ensure the energy demand for hydrogen production, and through the configuration of energy storage equipment and the flexible adjustment capability of its own equipment, it can achieve the balance of system power and electricity and the balance of hydrogen production and sales.

[0003] At present, energy system optimization planning aims to take economy as the main goal, conduct technical, environmental and economic evaluation of the system, and make reasonable investment planning for resources to achieve flexible and reliable operation of the system. Existing research aims to design power system optimization and control algorithms in the scenario of electric hydrogen energy system, so as to make full use of the potential of renewable energy and reduce power abandonment. Another part of the research focuses on the energy migration characteristics of medium- and long-term hydrogen storage in electric hydrogen energy system. However, the electric hydrogen energy system presents complex time coupling characteristics. How to fully consider the supply and demand balance requirements of heterogeneous energy and the differentiated operation characteristics of long / short-term hydrogen storage in the optimization operation is the key to achieve economic and efficient operation of the electric hydrogen energy system. At the same time, existing research has not discussed the impact of potential risk costs caused by uncertain factors in the system operation process on the optimization planning results. Therefore, for the planning of electric hydrogen energy system, the key difficulty lies in comprehensively considering the long-term operation characteristics and potential operation risks of the electric hydrogen energy system, and making reasonable capacity optimization planning. Summary of the invention

[0004] The purpose of the present invention is to provide a risk defense planning method for an electric hydrogen energy system based on adaptive time series aggregation, which can simultaneously consider the regulation capacity of long / short cycle hydrogen storage and effectively balance the potential operating risks and investment costs of the system.

[0005] To achieve the above object, the present invention provides a risk defense planning method for an electric hydrogen energy system based on adaptive time series aggregation, comprising the following steps:

[0006] S1. Based on the existing forecast values ​​of renewable energy and load demand, an adaptive time series aggregation algorithm is constructed to reduce the variable dimension of the running simulation;

[0007] S2. Introduce the conditional value at risk theory and use multivariate Gaussian distribution to model renewable energy and power load fluctuations to quantify the potential operational risks of the system;

[0008] S3. Taking into account both system investment and operating costs, and taking the minimization of annualized total costs as the optimization goal, an optimization planning model for the electric-hydrogen energy system based on adaptive time series aggregation is constructed;

[0009] S4. The electric-hydrogen energy system optimization planning model based on adaptive time series aggregation obtains the planned capacity and costs of each device, and completes the risk defense planning for the electric-hydrogen energy system.

[0010] Preferably, an adaptive time series aggregation algorithm is constructed based on the existing predicted values ​​of renewable energy and load demand, including the following steps:

[0011] Calculate the centroid of each cluster I:

[0012]

[0013] Where N I is the number of data points in cluster I, x t Indicates the new energy output data;

[0014] For each pair of adjacent clusters (I, J), calculate the dissimilarity D(I, J):

[0015]

[0016] In the formula, represents the Euclidean distance between the centroids of clusters I and J, N J represents the number of data points in cluster J;

[0017] Merge the two adjacent clusters with the smallest dissimilarity into one cluster, and update the number of clusters n=n-1, n≤R, where R represents the time resolution, and terminate the loop, otherwise return to step S2;

[0018] Output Based on the clustering results, a scheduling cycle lasting multiple days is obtained. For each day j in cluster I, the Euclidean distance between the load curve of that day and the centroid of the cluster is calculated. All daily load curves in the cluster are traversed and the minimum distance d is recorded.

[0019]

[0020] Where P(j) represents the load on the jth day, [t] represents the tth hour, and N i Indicates the total number of days;

[0021] The corresponding j-th day is set as a representative day. For typical days in the four seasons, an adaptive time series aggregation algorithm is applied to perform cluster analysis on the renewable energy output data in the four seasons.

[0022] Preferably, a multivariate Gaussian distribution is used to model renewable energy and electrical load fluctuations, including

[0023] Construct a multivariate Gaussian distribution to describe the probability distribution of renewable energy and electric load fluctuations. Set X to represent the joint matrix of renewable energy power generation and grid load, where X1 is renewable energy power generation and X2 is grid load. Then the joint distribution of X is described as a multivariate Gaussian distribution:

[0024]

[0025]

[0026]

[0027] Where μ is the mean vector, μ1 and μ2 are the average values ​​of renewable energy generation and load, Σ is the covariance matrix, and are the variances of renewable energy power generation and electric load, respectively, and ρ is the correlation coefficient between the two.

[0028] Preferably, the conditional risk value theory is calculated in the following way in multi-scenario simulation and general loss distribution:

[0029]

[0030] represents the confidence level, an indicator to measure the risk of investment loss, y represents the uncertainty variable controlled by the probability distribution function P, [f(x,y s )-VaR] + represents max{f(x,y s )-VaR,0}, N represents the total number of scenes, p s represents the probability of scene s appearing, y s A variable representing the cost of scene s.

[0031] Preferably, the optimization goal is to minimize the annualized total cost.

[0032] min obj=C inv +[(1-β)(C opt +C shed )+βCVaR] (9);

[0033] Where obj represents the objective function, C inv C is the annualized investment cost within the planning period n years taking into account the equipment discount rate, opt is the average annual operating cost, C shed To reduce the cost of hydrogen load, (1-β)(C opt +C shed )+βCVaR represents the trade-off between the operating cost and the corresponding expected risk loss under all scenarios, β represents the risk parameter, β∈(0,1).

[0034] Preferably, the planning model based on adaptive time series aggregation includes an equipment capacity planning link for making investment decisions on the system and an operation simulation link for measuring the system operation cost;

[0035] In the investment decision-making stage, capacity planning is carried out for the three types of equipment in the electric hydrogen energy system, namely the electrolyzer, short-cycle hydrogen storage and long-cycle hydrogen storage, and passed to the operation stage as the equipment operation boundary; in the operation simulation stage, based on the adaptive timing aggregation results, the system is simulated for the whole year to obtain the optimal scheduling result of the system.

[0036] Preferably, the short-term hydrogen storage SHS constraints include energy storage SOC constraints and hydrogen charging and discharging constraints:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] In the formula, are the SOH of SHS at time t and t+1 respectively; are the volume of hydrogen charged and discharged at time t; δ SHS is the self-consumption rate of SHS; V SHS Planning capacity for SHS; are the hydrogen charging and discharging efficiencies of SHS respectively; They are the upper and lower limits of SHS capacity percentage respectively; They represent the hydrogen charging and discharging states of SHS at time t respectively; They are the capacity percentages of SHS at the first and last moments of hydrogen charging and discharging, respectively.

[0043] Preferably, the hydrogen storage SOH of the long-term hydrogen storage LHS remains continuous between different seasons a, and the hydrogen storage SOH is the same at the beginning and end of the year. In any scheduling cycle, the LHS is evenly charged / discharged with hydrogen.

[0044]

[0045]

[0046] In the formula, is the hydrogen filling / discharging quality of LHS in a certain scheduling period, T H is the total number of hours in the scheduling cycle. The remaining variables and operating constraints can be compared to SHS. represents the charging / discharging hydrogen mass of LHS at time t, They are the capacity percentages of LHS at the first and last moments of hydrogen charging and discharging, respectively.

[0047] Preferably, in terms of energy balance, a DC power flow model is used for power flow constraints.

[0048] P DG ≥P load +P EL (17);

[0049] G EL +G SHS.d +G LHS.d ≥G load +G shed +G SHS.c +G LHS.c (18);

[0050] Where P DG Represents the power generated by renewable energy, P load Indicates load power, P EL Indicates the electrolytic cell power, G EL Indicates the output of the electrolytic cell, G SHS.d Indicates the mass of hydrogen released during short-term energy storage, G LHS.d Indicates the mass of hydrogen released during long-term energy storage, G load Indicates load, G shed Indicates load shedding, G SHS.c Indicates the quality of hydrogen filling for short-cycle energy storage.

[0051] Therefore, the present invention adopts the above-mentioned risk defense planning method for the electric hydrogen energy system based on adaptive time series aggregation, which can simultaneously consider the regulation capacity of long / short cycle hydrogen storage and effectively balance the potential operating risks and investment costs of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of a risk defense planning method for an electric hydrogen energy system based on adaptive time series aggregation according to the present invention;

[0053] Figure 2 Schematic diagram of a planning-operation model based on adaptive timing aggregation in an embodiment of the present invention;

[0054] Figure 3 This is the adaptive time series aggregation result in spring in the embodiment of the present invention;

[0055] Figure 4 This is the adaptive time series aggregation result in the summer season in the embodiment of the present invention;

[0056] Figure 5This is the adaptive time series aggregation result of the autumn season in the embodiment of the present invention;

[0057] Figure 6 This is the adaptive time series aggregation result for the winter season in the embodiment of the present invention;

[0058] Figure 7 The new energy prediction and actual output curve in the embodiment of the present invention;

[0059] Figure 8 The following is a comparison chart of actual results under different confidence levels and risk preferences. DETAILED DESCRIPTION

[0060] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0061] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0062] Embodiment 1

[0063] like Figure 1 As shown, a risk defense planning method for an electric hydrogen energy system based on adaptive time series aggregation includes the following steps:

[0064] Step S1: Based on the forecast values ​​of renewable energy and load demand obtained from historical data, an adaptive time series aggregation algorithm is designed to reduce the variable dimension of the running simulation.

[0065] An adaptive time series aggregation algorithm was constructed that can support operation simulation with an annual cycle while reducing the complexity of the model to a certain extent and reflecting the system operation characteristics in different seasons.

[0066] There are three key parameters in adaptive timing aggregation, namely, scheduling period H, time resolution R and look-ahead coefficient L (L∈(0,1)).

[0067] The clustering algorithm steps are as follows:

[0068] Input the parameters H, R and L required by the algorithm and the annual renewable energy output data;

[0069] Initialize the number of clusters n with the total number of data points.

[0070] Calculate the centroid of each cluster I:

[0071]

[0072] Where N I is the number of data points in cluster I, x t Represents new energy output data.

[0073] For each pair of adjacent clusters (I, J), calculate the dissimilarity D(I, J):

[0074]

[0075] In the formula, represents the Euclidean distance between the centroids of clusters I and J, N J represents the number of data points in cluster J.

[0076] Merge the two adjacent clusters with the smallest dissimilarity into one cluster, and update the number of clusters n = n-1. If n≤R, R represents the time resolution, terminate the loop, otherwise return to step S2.

[0077] Output Based on the clustering results, a scheduling period lasting multiple days is obtained. For each day j in cluster I, the Euclidean distance between the load curve of that day and the cluster centroid is calculated.

[0078] Traverse all daily load curves in the cluster and record the minimum distance d.

[0079]

[0080] Where P(j) represents the load on the jth day, [t] represents the tth hour, and N i Indicates the total number of days;

[0081] Set the corresponding j-th day as the representative day.

[0082] For typical days in four seasons, the above algorithm was applied to perform cluster analysis on the renewable energy output data in four seasons. The results are as follows: Figures 3 to 6 As shown, the feasibility of the adaptive temporal aggregation algorithm is also confirmed.

[0083] Step S2: introduce the conditional value at risk theory and use multivariate Gaussian distribution to model renewable energy and power load fluctuations to quantify the potential operating risks of the system.

[0084] The existence of uncertainties in the power system, such as renewable energy and load fluctuations, may lead to potential risk costs in system operation. Value-at-risk (VaR) and conditional value-at-risk (CVaR) are usually used as methods to quantify risks in the financial field.

[0085] VaR is a measure of the risk of investment loss, estimating how much a portfolio of investments may lose under given probability, given market conditions, and in a given period of time.

[0086]

[0087] In formula (4), x is the investment decision variable representing the investment portfolio, represents the investment cost, f(x, y) represents the loss function, and the vector y represents the uncertainty variable controlled by the probability distribution function P. For a certain x, Ψ(x, ·) represents the accumulated loss of f(x, y). Formula (5) is the general definition of VaR, and α is the confidence level.

[0088] Compared to VaR, CVaR quantifies the average loss of impossible scenarios that exceed the confidence level within a specified time. As a function of x and VaR, F(x,VaR) is convex and continuously differentiable, which means that for a given confidence level α, the CVaR associated with the investment decision vector x is determined by formulas (6)-(7):

[0089]

[0090] QUR α =minF(x,VaR α ) (7);

[0091] In the formula, CVaR ɑ represents the conditional value at risk, [f(x,y)-VaR] + represents max{f(x,y)-VaR,0}, and p(y) represents the probability density function of y. It is calculated in the following way in multi-scenario simulation and general loss distribution:

[0092]

[0093] In the formula, N represents the total number of scenes, p s represents the probability of scene s appearing, y s A variable representing the cost of scene s.

[0094] In order to consider the uncertainty of both renewable energy and electricity load, a multivariate Gaussian distribution is constructed to describe the probability distribution of these two variables. Let X represent the joint matrix of renewable energy generation and grid load, where X1 is renewable energy generation and X2 is grid load. Then the joint distribution of X can be described as a multivariate Gaussian distribution:

[0095]

[0096]

[0097] μ=[μ1,μ2] T (11);

[0098]

[0099] Where μ is the mean vector, μ1 and μ2 are the average values ​​of renewable energy generation and load, respectively. Σ is the covariance matrix, and The variance ρ of renewable energy power generation and electric load, respectively, is the correlation coefficient between the two. By examining their covariance matrix Σ, we can understand the joint behavior and mutual influence of these two variables.

[0100] Step S3: Taking into account both system investment and operating costs, minimizing the annualized total cost is taken as the optimization goal, and an electric-hydrogen energy system optimization planning model based on adaptive time series aggregation is constructed.

[0101] The planning model based on adaptive time series aggregation includes the equipment capacity planning link for making investment decisions on the system and the operation simulation link for measuring the system operation cost, such as Figure 2 As shown, Figure 2 The simulation results for the four seasons are as follows: Figures 3 to 6 shown.

[0102] In the investment decision-making stage, capacity planning is carried out for the three types of equipment in the electric hydrogen energy system, namely the electrolyzer, short-cycle hydrogen storage and long-cycle hydrogen storage, and passed to the operation stage as the equipment operation boundary; in the operation simulation stage, based on the adaptive timing aggregation results, the system is simulated for the whole year to obtain the optimal scheduling result of the system.

[0103] The objective function of the adaptive time-series aggregation electric hydrogen energy system optimization planning model is to minimize the sum of investment cost, operating cost and hydrogen load cost, as shown in the following formula.

[0104] min obj=C inv +C opt +C shed (13);

[0105]

[0106] Where obj represents the objective function, C inv is the annualized investment cost within the planning period n years taking into account the equipment discount rate, r is the discount rate, c inv.j is the investment cost of the jth equipment, V j is the investment capacity of the jth equipment, C opt is the average annual operating cost, C shed To reduce the cost of hydrogen load, the total operating cost of each dispatch period is represented by the product of the operating cost of the representative day and the number of days.

[0107] The final objective function considering CVaR is shown in formula (15):

[0108] min obj=C inv+[(1-β)(C opt +C shed )+βCVaR] (15);

[0109] In the formula, the second term (1-β)(C opt +C shed )+βCVaR represents the trade-off between the operating cost and the corresponding expected risk loss under all scenarios. Considering different values ​​of the risk parameter β between 0 and 1 to cope with the risk aversion of different investors, the closer the value of β is to 1, the greater the significance of the risk, i.e., CVaR.

[0110] Although alkaline water electrolysis (AWE) technology is relatively mature, this embodiment will consider using a proton exchange membrane cell (PEM) because it is a better choice in terms of cost and efficiency in the future. The operation constraints of the electrolysis (EL) mainly include output upper and lower limit constraints, ramp constraints, and start-stop constraints.

[0111] Short-term hydrogen storage (SHS) mainly performs intraday regulation. Its constraints include energy storage SOC (state of charge, SOC) constraints, hydrogen charging and discharging constraints, etc.:

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] In the formula, are the SOH of SHS at time t and t+1 respectively; are the volume of hydrogen charged and discharged at time t; δ SHS is the self-consumption rate of SHS; V SHS Planning capacity for SHS; are the hydrogen charging and discharging efficiencies of SHS respectively; They are the upper and lower limits of SHS capacity percentage respectively; They represent the hydrogen charging and discharging states of SHS at time t respectively; They are the capacity percentages of SHS at the first and last moments of hydrogen charging and discharging, respectively.

[0118] Short-term hydrogen storage (LHS) mainly performs cross-seasonal regulation. The hydrogen storage SOH should be continuous between different seasons a, and the hydrogen storage SOH should be the same at the beginning and end of the year. It should be noted that by default, LHS charges / discharges hydrogen evenly within any scheduling cycle.

[0119]

[0120]

[0121] In the formula, is the hydrogen filling / discharging quality of LHS in a certain scheduling period, T H is the total number of hours in the scheduling cycle. The remaining variables and operating constraints can be compared to SHS. represents the charging / discharging hydrogen mass of LHS at time t, They are the capacity percentages of LHS at the first and last moments of hydrogen charging and discharging, respectively.

[0122] In terms of energy balance, both electricity and hydrogen energy need to meet the supply and demand matching constraints. The DC power flow model is used for power flow constraints.

[0123] P DG ≥P load +P EL (twenty three);

[0124] G EL +G SHS.d +G LHS.d ≥

[0125] G load +G shed +G SHS.c +G LHS.c (twenty four);

[0126] Where P DG Represents the power generated by renewable energy, P load Indicates load power, P EL Indicates the electrolytic cell power, G EL Indicates the output of the electrolytic cell, G SHS.d Indicates the mass of hydrogen released during short-term energy storage, G LHS.d Indicates the mass of hydrogen released during long-term energy storage, G load Indicates load, G shed Indicates load shedding, G SHS.c Indicates the quality of hydrogen filling for short-cycle energy storage.

[0127] This embodiment sets four comparison schemes to verify the effectiveness of the scheme proposed in the present invention:

[0128] Option 1: Deterministic planning model. Consider the output characteristics of new energy on typical days in four seasons, only consider the regulatory role of short-term hydrogen storage, and formulate a planning model based on the operation simulation of typical days;

[0129] Option 2: Planning model based on CVaR risk quantification, the rest is the same as Option 1;

[0130] Option 3: Deterministic planning model. Adopting the adaptive time series aggregation method and considering the regulatory role of long- and short-term hydrogen storage, a planning model is formulated based on the full-year operation simulation;

[0131] Plan 4: Planning model based on CVaR risk quantification, the rest is the same as Plan 3.

[0132] New energy forecast and actual output curve Figure 4 As shown, the hourly hydrogen load is a constant value (10 t / h).

[0133] Table 1 compares the planning results under the four schemes, including equipment capacity planning results and various cost values. The planning results show that in Schemes 3 and 4, which can utilize the long-term hydrogen storage regulation capacity, the long-term hydrogen storage capacity is significantly higher than the short-term hydrogen storage, thanks to its investment economy and the advantages of smoothing seasonal electricity imbalance. Comparing Scheme 2 with Scheme 1 (Scheme 4 with Scheme 3), it can be found that the model including CVaR can reduce the overall cost through a more sophisticated risk management strategy. At the same time, combining the full-year operation simulation and CVaR quantified risk can reduce the risk cost in an uncertain environment, making the planning results more robust and reliable.

[0134] Table 1 Comparison of planning results (α=0.90,β=0.5)

[0135]

[0136]

[0137] Table 2 shows the comparison of planning results with adjusted risk parameter β. It can be found that investment cost and operation and maintenance cost increase with the increase of risk parameter, while load shedding cost decreases with the increase of risk parameter, indicating that the demand for load shedding decreases under higher risk parameter. Therefore, increasing risk parameter can more effectively utilize long-term hydrogen storage to deal with load fluctuation and uncertainty of renewable energy output.

[0138] Table 2 Comparison of planning results under different risk parameters (Scheme 4)

[0139]

[0140] Figure 5The figure is a comparison of the actual results of different confidence levels α and different adjusted risk preferences β. It can be found that the total cost increases with the increase of confidence level, indicating that the average loss of impossible scenarios exceeding the confidence level within the specified time increases, which shows that reducing the confidence level can improve the economy of the system.

[0141] Therefore, the present invention adopts the above-mentioned risk defense planning method for the electric hydrogen energy system based on adaptive time series aggregation, which can simultaneously consider the regulation capacity of long / short cycle hydrogen storage and effectively balance the potential operating risks and investment costs of the system.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A risk defense planning method for electric hydrogen energy system based on adaptive time series aggregation, characterized in that: The following steps are involved: S1. Based on the existing forecast values ​​of renewable energy and load demand, an adaptive time series aggregation algorithm is constructed to reduce the variable dimension of the running simulation; S2. Introduce the conditional value at risk theory and use multivariate Gaussian distribution to model renewable energy and power load fluctuations to quantify the potential operational risks of the system; S3. Taking into account both system investment and operating costs, and taking the minimization of annualized total costs as the optimization goal, an optimization planning model for the electric-hydrogen energy system based on adaptive time series aggregation is constructed; S4. The electric-hydrogen energy system optimization planning model based on adaptive time series aggregation obtains the planned capacity and costs of each device, and completes the risk defense planning for the electric-hydrogen energy system.

2. According to claim 1, a risk defense planning method for an electric hydrogen energy system based on adaptive time series aggregation is characterized in that: According to the existing forecast values ​​of renewable energy and load demand, an adaptive time series aggregation algorithm is constructed, which includes the following steps: Calculate the centroid of each cluster I: Where N I is the number of data points in cluster I, x t Indicates the new energy output data; For each pair of adjacent clusters (I, J), calculate the dissimilarity D(I, J): In the formula, represents the Euclidean distance between the centroids of clusters I and J, N J represents the number of data points in cluster J; Merge the two adjacent clusters with the smallest dissimilarity into one cluster, and update the number of clusters n=n-1, n≤R, where R represents the time resolution, and terminate the loop, otherwise return to step S2; Output Based on the clustering results, a scheduling cycle lasting multiple days is obtained. For each day j in cluster I, the Euclidean distance between the load curve of that day and the centroid of the cluster is calculated. All daily load curves in the cluster are traversed and the minimum distance d is recorded. Where P(j) represents the load on the jth day, [t] represents the tth hour, and N i Indicates the total number of days; The corresponding j-th day is set as a representative day. For typical days in the four seasons, an adaptive time series aggregation algorithm is applied to perform cluster analysis on the renewable energy output data in the four seasons.

3. According to claim 1, a risk defense planning method for an electric hydrogen energy system based on adaptive time series aggregation is characterized in that: Modeling of renewable energy and electrical load fluctuations using multivariate Gaussian distribution, including Construct a multivariate Gaussian distribution to describe the probability distribution of renewable energy and electric load fluctuations. Set X to represent the joint matrix of renewable energy power generation and grid load, where X1 is renewable energy power generation and X2 is grid load. Then the joint distribution of X is described as a multivariate Gaussian distribution: μ=[μ1,μ2] T (6); Where μ is the mean vector, μ1 and μ2 are the average values ​​of renewable energy generation and load, Σ is the covariance matrix, and are the variances of renewable energy power generation and electric load, respectively, and ρ is the correlation coefficient between the two.

4. According to claim 1, a risk defense planning method for an electric hydrogen energy system based on adaptive time series aggregation is characterized in that: The conditional VaR theory, in a multi-scenario simulation and general loss distribution, is calculated as follows: Where F(x,VaR) is the function of x and VaR, VaR is the value at risk, α is the confidence level, which is an indicator to measure the risk of investment loss, and y is the uncertainty variable controlled by the probability distribution function P. s )-VaR] + represents max{f(x,y s )-VaR,0}, N represents the total number of scenes, p s represents the probability of scene s appearing, y s A variable representing the cost of scene s.

5. According to claim 1, a risk defense planning method for an electric hydrogen energy system based on adaptive time series aggregation is characterized in that: The optimization goal is to minimize the annualized total cost. min volume C inv +[(1-β)(C opt +C shed )+βCVaR] (9); Where obj represents the objective function, C inv C is the annualized investment cost within the planning period n years taking into account the equipment discount rate, opt is the average annual operating cost, C shed To reduce the cost of hydrogen load, (1-β)(C opt +C shed )+βCVaR represents the trade-off between the operating cost and the corresponding expected risk loss under all scenarios, β represents the risk parameter, β∈(0,1).

6. According to claim 1, a risk defense planning method for an electric hydrogen energy system based on adaptive time series aggregation is characterized in that: The planning model based on adaptive time series aggregation includes equipment capacity planning for system investment decisions and operation simulation for measuring system operation costs; In the investment decision-making stage, capacity planning is carried out for the three types of equipment in the electric hydrogen energy system, namely the electrolyzer, short-cycle hydrogen storage and long-cycle hydrogen storage, and passed to the operation stage as the equipment operation boundary; in the operation simulation stage, based on the adaptive timing aggregation results, the system is simulated for the whole year to obtain the optimal scheduling result of the system.

7. The method for risk defense planning of an electric hydrogen energy system based on adaptive time series aggregation according to claim 6 is characterized in that: Short-term hydrogen storage (SHS) constraints include energy storage SOC constraints and hydrogen charging and discharging constraints: In the formula, are the SOH of SHS at time t and t+1 respectively; are the volume of hydrogen charged and discharged at time t; δ SHS is the self-consumption rate of SHS; V SHS Planning capacity for SHS; are the hydrogen charging and discharging efficiencies of SHS respectively; They are the upper and lower limits of SHS capacity percentage respectively; They represent the hydrogen charging and discharging states of SHS at time t respectively; They are the capacity percentages of SHS at the first and last moments of hydrogen charging and discharging, respectively.

8. The method for risk defense planning of an electric hydrogen energy system based on adaptive time series aggregation according to claim 6 is characterized in that: The long-term hydrogen storage LHS has a continuous hydrogen storage SOH between different seasons a, and the hydrogen storage SOH is the same at the beginning and end of the year. In any scheduling cycle, the LHS is evenly charged / discharged with hydrogen In the formula, is the hydrogen filling / discharging quality of LHS in a certain scheduling period, T H is the total number of hours in the scheduling cycle. The remaining variables and operating constraints can be compared to SHS. represents the charging / discharging hydrogen mass of LHS at time t, They are the capacity percentages of LHS at the first and last moments of hydrogen charging and discharging, respectively.

9. The method for risk defense planning of an electric hydrogen energy system based on adaptive time series aggregation according to claim 6 is characterized in that: In terms of energy balance, the DC power flow model is used for power flow constraints. P DG ≥P load +P EL (17); G EL +G SHS.d +G LHS.d ≥G load +G shed +G SHS.c +G LHS.c (18); Where P DG Represents the power generated by renewable energy, P load Indicates load power, P EL Indicates the electrolytic cell power, G EL Represents the output of the electrolytic cell, G SHS.d Indicates the mass of hydrogen released during short-term energy storage, G LHS.d Indicates the mass of hydrogen released during long-term energy storage, G load Indicates load, G shed Indicates load shedding, G SHS.c Indicates the quality of hydrogen filling for short-cycle energy storage.

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