Multi-energy system safety response collaborative resisting method based on voltage sensitivity identification

Abstract

The invention relates to the technical field of new energy safety operation, in particular to a multi-energy system safety response collaborative defense method based on voltage sensitivity identification, and provides a voltage sensitivity identification method to realize differential risk perception in extremely cold weather by constructing a mobile energy storage unit model and fully mining the advantages of multi-energy interactive operation of the mobile energy storage unit model. And determining the access position of the mobile energy storage unit in the risk scene, constructing a risk resistance evaluation model, and verifying the reasonability and feasibility of the proposed method. The identification method based on the voltage sensitivity can effectively improve the positioning accuracy of the mobile energy storage unit and improve the risk response capability of the multi-energy system; the safety and economy of system operation in a risk scene can be effectively measured through a risk assessment and benefit analysis model, the voltage level of the system is effectively improved, and the anti-risk capability of the multi-energy system is remarkably enhanced; the mobile energy storage unit in a multi-energy storage form can quickly and flexibly respond to system requirements, and economic losses are effectively reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy safe operation, and particularly relates to a multi-energy system safety response collaborative defense method based on voltage sensitivity identification. BACKGROUND

[0002] Under the "double carbon" target, the penetration rate of distributed power sources in the multi-energy system continues to increase. However, the volatility and intermittency of new energy (wind and light power sources) output significantly increase the risk factors of voltage out-of-limit in the multi-energy system. With the frequent occurrence of extremely cold weather (such as cold wave, freezing rain, etc.), the out-of-limit risk is aggravated, affecting the safe and stable operation of the multi-energy system. SUMMARY

[0003] The purpose of the present application is to overcome the defects of the prior art and provide a multi-energy system safety response collaborative defense method based on voltage sensitivity identification, which can improve the response ability and risk resistance ability of the multi-energy system to risks, quickly and flexibly respond to system requirements, and effectively reduce economic losses.

[0004] To achieve the above-mentioned purpose, the present application adopts the following specific technical solutions:

[0005] The multi-energy system safety response collaborative defense method based on voltage sensitivity identification provided by the present application comprises the following steps:

[0006] Step one, a mobile energy storage unit model is constructed, including a multi-energy storage module, a transportation carrier module and an energy flow coupling module; through the design of energy storage-transportation-energy supply / usage, dynamic collaborative defense means are provided for the multi-energy system;

[0007] Step two, weak link identification based on voltage sensitivity, including voltage sensitivity calculation based on median segmentation method, source-grid-load three-side risk sensitivity perception, weak link identification and mobile energy storage access position determination; the voltage sensitivity is used to identify the weak link under extremely cold weather, complete the overall situation awareness of the system, and determine the access position of the mobile energy storage unit to provide energy support for risk power supply;

[0008] Step three, a risk assessment and benefit analysis model is constructed, a safety index is constructed by using voltage deviation degree to measure the power supply quality of the mobile energy storage unit; a risk economic index is constructed by using loss of load to represent the load guarantee capacity of the system.

[0009] Further, in step one, the multi-energy storage module considers the volatility and intermittency of wind and light power sources, uses its throughput capacity to relieve energy supply pressure, and uses its charge and discharge characteristics to smooth load fluctuations; the response condition and constraint condition of the multi-energy storage module are as follows:

[0010] Further, in step one, the multi-energy storage module considers the volatility and intermittency of wind and light power sources, uses its throughput capacity to relieve energy supply pressure, and uses its charge and discharge characteristics to smooth load fluctuations; the response condition and constraint condition of the multi-energy storage module are as follows:

[0011] In the formula, E i,t Let E be the capacity of the i-th type of multi-energy storage module at time t. max E min These are the upper and lower limits of the capacity of the multi-energy storage module, respectively. These represent the charging and discharging power of the i-th type of multi-energy storage module at time t; These represent the upper and lower limits of the charging power of the multi-energy storage module. These are the upper and lower limits of the power supply for the multi-energy storage module, respectively. These are the charging and discharging efficiencies of the i-th type of multi-energy storage module at time t, respectively. These represent the charging and discharging states of the multi-energy storage module at time t, with 0 indicating a non-working state and 1 indicating a working state.

[0012] Furthermore, in step one, the energy flow coupling module includes an electro-hydrogen coupling unit and a hydrogen energy derivative unit; the electro-hydrogen coupling unit (SOEC / SOFC) is specifically as follows:

[0013]

[0014] In the formula, These represent the power supplied by SOEC and SOFC at time t, respectively. These represent the hydrogen production power of SOEC and the hydrogen consumption power of SOFC at time t, respectively; η SOEC and η SOFC The working efficiency of SOEC and SOFC, respectively; This is the higher heating value of hydrogen. ξ represents the heating power supplied by the SOFC at time t. SOFC These are the conversion factors;

[0015] The hydrogen energy derivative unit utilizes hydrogen and nitrogen generated by SOEC electrolysis to complete the ammonia synthesis reaction through an electro-ammonia conversion device, and then combines with carbon dioxide to complete the methanation process. The chemical reaction process is as follows:

[0016] N2 + 3H2 → 2NH3;

[0017] CO2 + 4H2 → CH4 + 2H2O;

[0018] The ammonia synthesis rate is limited by pressure and temperature, showing a negative correlation with temperature and a positive correlation with pressure. The ammonia synthesis rate and the ammonia fuel cell model are as follows:

[0019]

[0020] In the formula, ν is the ammonia synthesis rate; P is the pressure of each gas; ν is the correction factor, taken as 4.75; ρ is the density of the ammonia synthesis catalyst; k1, k -1 T is the pre-exponential factor;r is the room temperature; is the ammonia fuel cell power generation; is the ammonia consumption power of SOFC at time t; is the high heat value of ammonia gas;

[0021] Considering the energy consumption, hydrogen consumption and methane generation rate in the methanation process, a methanation model is constructed according to the molar relationship of the chemical reaction process as follows:

[0022]

[0023] In the formula, are the methane output and hydrogen consumption in the methanation process, respectively, is the methane consumption in the methanation process; are the output heat energy and power consumption in the methanation process, respectively; are the heat conversion and electricity-to-gas efficiency, respectively; is the methane heat value.

[0024] Further, in step one, the transportation carrier module is specifically as follows:

[0025] ∑ i∈N x i,k (t)≤1;

[0026]

[0027] z m (t)=1-∑ i∈N x i,k ;

[0028]

[0029] In the formula, x i,k is the connection state of the kth mobile energy storage unit at node i, and its value is 1, indicating that the mobile energy storage unit is connected to node i, and the value is 0, indicating that the mobile energy storage unit is not connected to node i; is the maximum number of mobile energy storage units that node i can access; z m (t) is the driving state of the mobile energy storage unit, and its value is 1, indicating that the mobile energy storage unit is driving on the road; T ij is the driving time of the mobile energy storage unit from node i to node j.

[0030] Further, in step two, the voltage sensitivity calculation based on the median segmentation method is specifically as follows:

[0031]

[0032] In the formula, K ij,tSensitivity of node i voltage to node j power change at time t; Sensitivity of node i voltage to node j power change at time t; i,t And Vi(t) and Vi*(t) are the actual voltage and its expected value of node i at time t, respectively;

[0033] Considering the influence of wind and light fluctuations and abnormal behavior of load on the system in extremely cold weather, the degree of voltage deviation of each node in the system is studied, the median segmentation method is introduced, and the median in the sensitivity matrix column is selected as the judgment standard to determine the sensitivity of node j to the entire system The sensitivity calculation method of the mobile energy storage unit access node is as follows:

[0034]

[0035] In the formula, The median of the jth column of the sensitivity matrix at time t; The sum of the sensitivities of the jth column of the sensitivity matrix at time t that are higher than the median; The sum of the sensitivities of the jth column of the sensitivity matrix at time t that are lower than the median, The sensitivity of node j to the voltage of the entire system.

[0036] Further, in the source-grid-load three-side risk sensitivity awareness in step two, the source-side risk voltage sensitivity index K source The calculation method is as follows:

[0037]

[0038] In the formula, The limited wind and light power at time t; The wind and light power of the typical day at time t; λ source,t The risk weight coefficient of the source at time t, when the wind and light loss rate is 0, λ source,t =1 indicates that the risk scenario at time t has no effect on the source side;

[0039] The grid-side risk voltage sensitivity index K grid As follows:

[0040]

[0041] L I,t =9.8×10 -3 ρ i π(D+d t )d t ;

[0042]

[0043] In the formula, d t The cumulative icing thickness at time t; ΔT is 15 min; ρ i r is the density of ice. r For rainfall rate; ρ w The density of water; v t Let L be the wind speed at time t; I,t and L W,t Here, represents the ice load and wind load per unit length of the line at time t; D is the line diameter, which is 10 mm; E is the span factor; F... t P represents the icing wind load per unit length of the line at time t; b,t Let a be the probability of failure per unit length of line during time period t; IW and b IW These are the ice wind load threshold values ​​for normal operation and fault confidence level of the distribution line, respectively; A cut,t Let A be the number of branches with the probability of a line fault at time t; l λ represents the total number of branches; grid,t λ is the risk weighting coefficient for time period t on the network side; when the probability of disconnection is 0, λ grid,t =1 indicates that the impact of the risk scenario on the network side at time t is 0;

[0044] Load-side risk voltage sensitivity index K load as follows:

[0045]

[0046] In the formula, Let t be the amount of load loss; λ represents the rated load power at time t; load,t λ is the risk weighting coefficient at time t on the load side; when the load loss rate is 0, λ load,t A value of 1 indicates that the impact of the risk scenario on the load side at time t is 0;

[0047] In step two, the identification of weak points and the determination of the location for connecting mobile energy storage are as follows:

[0048]

[0049] In the formula, The risk weighting coefficients for the source-grid-load three sides are assigned using a composite weighting method.

[0050] Furthermore, in step three, the safety indicator Ψ1 and the risk-economic indicator Ψ2 are specified as follows:

[0051]

[0052] In the formula, U i,t U is the square of the actual voltage value of node i at time t. i,minand U i,max is the square of the minimum and maximum voltage of node i at time t, U i,N is the square of the rated voltage of node i; is the load loss at time t; P MESS,k,t is the active power of the kth mobile energy storage unit at time t; p lt is the load loss cost; K is the number of mobile energy storage units; the smaller ψ2 is, the stronger the load guarantee capability is.

[0053] The present application can achieve the following technical effects:

[0054] The present application builds a mobile energy storage unit model, including a multi-energy storage model, an energy flow coupling model, and a mobile transportation carrier model; completes weak link identification based on voltage sensitivity, completes three-side risk perception using source-grid-load three-side risk voltage sensitivity indicators, performs comprehensive weak link identification by fusing three-side indicators, realizes comprehensive situation awareness, determines the optimal access position of the mobile energy storage unit; builds a risk assessment and benefit analysis model to measure the safety response collaborative defense effect. Finally, the result verification shows that the identification method based on voltage sensitivity can effectively improve the positioning accuracy of the mobile energy storage unit and improve the response ability of the multi-energy system to risks; the risk assessment and benefit analysis model can effectively measure the safety and economy of system operation in the risk scenario, effectively improve the voltage level of the system, and significantly enhance the anti-risk ability of the multi-energy system; the mobile energy storage unit in the form of multi-energy storage can quickly and flexibly respond to system demand and effectively reduce economic losses. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 is a multi-energy system framework diagram in extremely cold weather according to an embodiment of the present application;

[0056] Figure 2 is a regional multi-energy system energy framework diagram according to an embodiment of the present application;

[0057] Figure 3 is a typical daily wind and load output diagram according to an embodiment of the present application;

[0058] Figure 4 is a risk voltage sensitivity diagram according to an embodiment of the present application;

[0059] Figure 5 is a voltage diagram of each node after optimization according to an embodiment of the present application;

[0060] Figure 6 is a mobile energy storage unit participation regulation output diagram according to an embodiment of the present application. DETAILED DESCRIPTION

[0061] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. In the following description, the same modules are denoted by the same reference numerals. In the case of the same reference numerals, their names and functions are also the same. Therefore, detailed descriptions thereof will not be repeated.

[0062] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not constitute a limitation on the present application.

[0063] The embodiment of the present application provides a multi-energy system safety response collaborative defense method based on voltage sensitivity identification, and the specific steps are as follows:

[0064] Step one, build a mobile energy storage unit model, including multi-energy storage module, transportation carrier module, and energy flow coupling module; through the design of energy storage-transportation-energy supply / use, provide dynamic collaborative defense means for multi-energy system.

[0065] The mobile energy storage unit is composed of multi-energy storage modules such as electricity, hydrogen, hydrogen, ammonia, and gas, transportation carriers, and energy flow coupling interfaces. Through the integrated design of energy storage-transportation-energy supply / use, its flexibility is fully tapped to provide dynamic collaborative defense means for multi-energy systems. Among them, the gas storage module considers the capacity and pressure of the gas storage tank, builds a multi-energy storage model, studies the energy flow coupling operation characteristics, and builds related models as shown below, while considering the constant-volume site selection of the mobile energy storage unit, and studying its access in the risk scenario.

[0066] 1. Multi-energy storage module Research on the response of multi-energy storage modules such as electricity, heat, hydrogen, hydrogen, ammonia, and gas, considering the volatility and intermittency of wind and solar power, using its throughput capacity to relieve energy supply pressure, and smoothing load fluctuations through its charge and discharge characteristics.

[0067] The response of the multi-energy storage module and the constraint condition are as follows:

[0068]

[0069] E min ≤E i,t ≤E max ; (2)

[0070]

[0071] In the formula, E i,t is the capacity of the i-th multi-energy storage module at time t, E max , and E min are the upper and lower limit values of the multi-energy storage module capacity, respectively. respectively are the charging and discharging power of the i-th multi-energy storage module at time t; respectively are the upper and lower limits of the charging power of the multi-energy storage module, respectively are the upper and lower limits of the power supply of the multi-energy storage module; respectively are the charging and discharging efficiency of the i-th multi-energy storage module at time t; respectively are the charging and discharging state of the multi-energy storage module at time t, taking 0 as the non-working state and 1 as the working state.

[0072] 2. The energy flow coupling module includes an electric-hydrogen coupling unit and a hydrogen energy derivation unit;

[0073] The electric-hydrogen coupling unit (SOEC / SOFC) is specifically as follows:

[0074]

[0075] In the formula, respectively are the power supply of SOEC and SOFC at time t; respectively are the hydrogen production power of SOEC and the hydrogen consumption power of SOFC at time t; η SOEC and η SOFC respectively are the working efficiencies of SOEC and SOFC; is the high calorific value of hydrogen; is the heat supply power of SOFC at time t; ξ SOFC is the conversion coefficient.

[0076] The hydrogen energy derivation unit uses the hydrogen produced by SOEC electrolysis and nitrogen to complete the synthesis of ammonia reaction through electric ammonia synthesis equipment, and combines with carbon dioxide to complete the methanation process, and the chemical reaction process is as follows:

[0077] N2+3H2→2NH3;

[0078] CO2+4H2→CH4+2H2O;

[0079] The synthesis ammonia rate is limited by pressure and temperature, and is negatively correlated with temperature and positively correlated with pressure, and the synthesis ammonia rate and ammonia fuel cell model are as follows:

[0080]

[0081] In the formula, is the synthesis ammonia rate; P is the pressure of each gas; v is a correction factor, taking 4.75; ρ is the density of the synthesis ammonia catalyst; k1, k -1 are the pre-exponential factors; T r is the room temperature; is the power generation of the ammonia fuel cell; is the SOFC ammonia consumption power at time t; High heat value of ammonia gas.

[0082] Considering the energy consumption, hydrogen consumption and methane generation rate in the methanation process, a methanation model is constructed according to the molar relationship of the chemical reaction process as follows:

[0083]

[0084] In the formula, respectively, the methane output and hydrogen consumption in the methanation process, is the methane consumption in the methanation process; respectively, the output heat energy and power consumption in the methanation process; respectively, the heat conversion and electricity-to-gas efficiency; is the methane heat value.

[0085] 3. The transport carrier module is as follows:

[0086] ∑ i∈N x i,k (t)≤1; (9)

[0087]

[0088] z m (t)=1-∑ i∈N x i,k ; (11)

[0089]

[0090] In the formula, x i,k is the connection state of the kth mobile energy storage unit at node i, and its value is 1, indicating that the mobile energy storage unit is connected to node i, and the value is 0, indicating that the mobile energy storage unit is not connected to node i; is the maximum number of mobile energy storage units that node i can access; z m (t) is the driving state of the mobile energy storage unit, and its value is 1, indicating that the mobile energy storage unit is driving on the road; T ij is the driving time of the mobile energy storage unit from node i to node j.

[0091] The multi-energy system framework under extremely cold weather is as shown in Figure 1 , and the subsequent research is taken as an example to study the electric heating multi-energy system, in which the electric energy system is a 33-node system, the thermal energy system is a 30-node system, the regional multi-energy system energy framework is as shown in Figure 2 , and the typical daily wind, light and load output are as shown in Figure 3 .

[0092] Step two, weak link identification based on voltage sensitivity. In order to study the problem of power supply guarantee in extremely cold weather, analyze the key factors such as voltage stability, and focus on using voltage sensitivity to identify weak links in extremely cold weather, complete the overall situation awareness of the system, and determine the access position of the mobile energy storage unit, provide energy support for risk supply guarantee. Weak link identification includes voltage sensitivity calculation based on median segmentation method, source-grid-load three-side risk sensitivity perception, and weak link identification and mobile energy storage access position determination.

[0093] 1. Voltage sensitivity calculation based on median segmentation method

[0094] Combined with the traditional sensitivity calculation method, considering the flexibility of the mobile energy storage unit, the voltage offset weight is introduced to optimize the traditional sensitivity, and the calculation method is as follows:

[0095]

[0096] In the formula, K ij,t is the sensitivity of node j power change to node i voltage at time t; is the traditional sensitivity of node j power change to node i voltage at time t; U i,t and are the actual voltage and expected value of node i at time t, respectively.

[0097] Considering the influence of wind and light fluctuation and abnormal behavior of load on the system in extremely cold weather, the offset degree of power change of a node to the voltage of each node of the system is studied, the median segmentation method is introduced, and the median in the sensitivity matrix column is selected as the judgment standard to determine the sensitivity of node j to the whole system The sensitivity calculation method of the mobile energy storage unit access node is as follows:

[0098]

[0099] In the formula, is the median of the jth column of the sensitivity matrix at time t; is the sum of the sensitivity of the jth column of the sensitivity matrix at time t which is higher than the median; is the sum of the sensitivity of the jth column of the sensitivity matrix at time t which is lower than the median, is the sensitivity of node j to the voltage of the whole system.

[0100] 2. Source-grid-load three-side risk sensitivity perception

[0101] 1) Source side

[0102] Affected by the different degrees of extremely cold weather, the output of the source-side wind-solar power appears differential reduction. Based on this, the loss power is predicted through environmental perception, and the risk weight of each period is represented by the wind-solar loss rate, and the source-side risk voltage sensitivity index K source The calculation method is as follows:

[0103]

[0104] In the formula, is the wind-solar limited power at time t; is the wind-solar power of the typical day at time t; λ source,t is the risk weight coefficient of the source at time t, when the wind-solar loss rate is 0, λ source,t = 1 indicates that the risk scenario at time t has no effect on the source side.

[0105] 2) Network side

[0106] The motor characteristics of the network side transmission network are greatly affected by the extremely cold weather, so the line break probability of the network side at each period is used to represent its risk weight, and the ratio of the number of branches with fault probability to the total number of branches is used to represent the risk weight. The network-side risk voltage sensitivity index K grid is as follows:

[0107]

[0108] L I,t = 9.8 × 10 -3 ρ i π(D+d t )d t ; (19)

[0109]

[0110] In the formula, d t is the cumulative ice thickness at time t; ΔT is 15 min; ρ i is the density of ice; r r is the rainfall rate; ρ w is the density of water; v t is the wind speed at time t; L I,t and L W,t are the ice force and wind force load per unit length of the line at time t, respectively; D is the line diameter, which is 10 mm; E is the span factor; F t is the ice and wind force load per unit length of the line at time t; P b,t is the fault probability per unit length of the line at time t; a IW and b IW are the ice and wind force load thresholds when the distribution line is normally operated and the confidence level is 100%; A cut,t is the number of branches with fault probability at time t; Al N is the total number of branches; λ grid,t λ is the risk weight coefficient of the grid side at time t; when the probability of disconnection is 0, λ grid,t = 1 indicates that the influence of the risk scenario at time t on the grid side is 0.

[0111] 3) Load side

[0112] The heating demand of the load side increases dramatically under extremely cold weather, which aggravates the peak-valley fluctuation and the degree of mismatch between supply and demand. By predicting and assessing the load loss, the flexibility of the mobile energy storage unit is used to improve the system power supply reliability. The load loss rate is used to represent the risk weight in each period, and the risk voltage sensitivity index K load is obtained as follows:

[0113]

[0114] In the formula, L is the load loss at time t; P is the rated load power at time t; λ load,t λ is the risk weight coefficient of the load side at time t; when the load loss rate is 0, λ load,t = 1 indicates that the influence of the risk scenario at time t on the load side is 0.

[0115] 3. Weak link identification and determination of mobile energy storage access location

[0116] To achieve comprehensive situational awareness of the system in the risk scenario, the source-grid-load three-side risk voltage sensitivity is integrated to build a comprehensive risk voltage sensitivity to identify weak links, and the response characteristics of the source-grid-load three-side risk weight are studied in depth to determine the access location of the mobile energy storage unit. The comprehensive risk voltage sensitivity calculation method is shown as follows:

[0117]

[0118] In the formula, λ is the risk weight coefficient of the source-grid-load three sides, and the weight coefficient is assigned by a composite weighting method.

[0119] The weak link identification of the system is combined with the comprehensive risk voltage sensitivity, and the node with the highest sensitivity value and its neighborhood are selected as the weak link to determine the access location of the mobile energy storage unit as the node with the highest sensitivity value, and the spatial layout optimization of the mobile energy storage unit is completed. To further analyze the safety response collaborative defense effect of the multi-energy system, a risk assessment and benefit analysis model is introduced.

[0120] Step three, build a risk assessment and benefit analysis model.

[0121] To further analyze the synergistic defense effect of the multi-energy system safety response, a risk assessment and benefit analysis model is constructed. The voltage excursion degree is used to construct the safety index to measure the power supply quality with the participation of mobile energy storage units; the lost load is used to construct the risk economic index to represent the system load guarantee capacity.

[0122] The safety index Ψ1 and the risk economic index Ψ2 are as follows:

[0123]

[0124] In the formula, U i,t is the square of the actual value of the voltage of node i at time t, U i,min and U i,max are the squares of the minimum and maximum values of the voltage of node i at time t, U i,N is the square of the rated voltage of node i; is the lost load at time t; P MESS,k,t is the active power of the kth mobile energy storage unit at time t; p lt is the cost of load loss; K is the number of mobile energy storage units; the smaller ψ2 is, the stronger the load guarantee capacity is.

[0125] By applying the above method steps, a three-stage strategy for mobile energy storage units to participate in synergistic defense in extremely cold weather is proposed, as follows:

[0126] ① Tap the flexible response capability of mobile energy storage units, study the working characteristics of multi-energy storage modules, and provide key energy support for risk supply.

[0127] ② Based on the risk perception, the source-grid-load sensitivity is used to represent the risk voltage sensitivity index, identify the weak links of the system, and determine the optimal access location of mobile energy storage units by integrating the three-side index.

[0128] ③ Construct a risk assessment and benefit analysis model, construct a safety index based on voltage excursion degree, construct a risk economic index combined with lost load, and solve it by using a quadratic cone programming algorithm to evaluate the risk operation benefit.

[0129] Analysis Figures 4 to 6 of the experimental results shows that:

[0130] 1) From the results of the experiment, it can be seen that: Figure 4It can be seen that the source side node 33 presents higher sensitivity at most times, but the risk voltage sensitivity value of node 31 is the highest. Since the weight proportion of the period when node 31 has higher sensitivity is relatively large, and the weight proportion of the period when node 33 has higher sensitivity is relatively small. Compared with the source side, the highest risk voltage sensitivity value of the network side is node 33. The result is derived from the differentiated risk weight adopted on both sides. The result shows that the weight based on the risk characteristics of each side can effectively optimize the site selection scheme. The sensitivity distribution of the load side node is obviously different from that of the source and network sides. Among them, the highest risk voltage sensitivity value of the network side is node 28, because the load reduction of the node has a coupling effect on the adjacent nodes, resulting in higher sensitivity value near the node. The method of the application introduces the risk factors of the source, network and load sides on the basis of improving the sensitivity, provides a decision basis more in line with the actual needs of the system for the site selection of the mobile energy storage unit, and improves the response ability of the multi-energy system to the risk.

[0131] 2) by Figure 5 It can be seen that through the optimization of the mobile energy storage unit, the voltage of each node of the system does not occur voltage out-of-limit phenomenon, verifying the effect of the mobile energy storage unit in resisting voltage drop and improving the voltage stability of the system in extremely cold weather. In addition, the voltage change curve of each node is relatively smooth, indicating that the reactive power regulation response of the mobile energy storage unit is stable and does not cause system oscillation.

[0132] 3) by Figure 6 It can be seen that at the beginning of the cold wave, the multi-form energy storage cooperates to store excess electric energy. With the intensification of the cold wave and the increase of the heating load, the system preferentially calls the heat storage unit to undertake the energy supply task, and then cooperates with the multi-form energy storage to supply power to ensure the key load. After the influence of the cold wave weakens, the multi-form energy storage turns to the charging mode to store excess electric energy in the power grid. The result shows that the multi-form energy storage under this strategy has multi-time scale flexible adjustment ability in extremely cold weather, significantly improves the system resilience and operation reliability, and reveals the dynamic response law of the multi-energy system in extremely cold weather, providing an important basis for optimizing the operation strategy.

[0133] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0134] Although the embodiments of the present application have been shown and described above, it is understood that the above-described embodiments are exemplary and are not to be construed as limiting the present application, and that those ordinarily skilled in the art can make various changes, modifications, substitutions and variations within the scope of the present application.

[0135] The above detailed description of the application is not to be construed as limiting the scope of the application. Any other corresponding changes and modifications of the above-described embodiments according to the technical concept of the present application are to be included within the scope of the present application.

Claims

1. A collaborative defense method for the safety response of a multi-energy system based on voltage sensitivity identification, characterized in that, Includes the following steps: Step 1: Construct a mobile energy storage unit model, including a multi-energy storage module, a transportation carrier module, and an energy flow coupling module; through the design of energy storage-transportation-supply / consumption, provide dynamic and coordinated defense measures for multi-energy systems; Step 2: Identification of weak links based on voltage sensitivity, including voltage sensitivity calculation based on median segmentation, risk sensitivity perception on the source-grid-load sides, identification of weak links and determination of mobile energy storage access locations; using voltage sensitivity to identify weak links under extreme cold weather, completing comprehensive system situational awareness, determining the access locations of mobile energy storage units, and providing energy support for risk-based supply assurance. Step 3: Construct a risk assessment and benefit analysis model. Use voltage deviation to build a safety index to measure the power supply quality of the mobile energy storage unit. Use load loss to build a risk-economic index to characterize the system's load guarantee capability.

2. The multi-energy system security response collaborative defense method based on voltage sensitivity identification according to claim 1, characterized in that, In step one, the multi-energy storage module considers the fluctuations and intermittent effects of wind and solar power, utilizing its throughput capacity to alleviate power supply pressure and its charge / discharge characteristics to smooth load fluctuations. The response and constraints of the multi-energy storage module are shown below: AND min ≤E i,t ≤E max ; In the formula, E i,t Let E be the capacity of the i-th type of multi-energy storage module at time t. max E min These are the upper and lower limits of the capacity of the multi-energy storage module, respectively. These represent the charging and discharging power of the i-th type of multi-energy storage module at time t; These represent the upper and lower limits of the charging power of the multi-energy storage module. These are the upper and lower limits of the power supply for the multi-energy storage module, respectively. These are the charging and discharging efficiencies of the i-th type of multi-energy storage module at time t, respectively. These represent the charging and discharging states of the multi-energy storage module at time t, with 0 indicating a non-working state and 1 indicating a working state.

3. The multi-energy system security response collaborative defense method based on voltage sensitivity identification according to claim 1, characterized in that, In step one, the energy flow coupling module includes an electro-hydrogen coupling unit and a hydrogen energy derivative unit; the electro-hydrogen coupling unit (SOEC / SOFC) is as follows: In the formula, These represent the power supplied by SOEC and SOFC at time t, respectively. These represent the hydrogen production power of SOEC and the hydrogen consumption power of SOFC at time t, respectively; η SOEC and η SOFC The working efficiency of SOEC and SOFC, respectively; This is the higher heating value of hydrogen. ξ represents the heating power supplied by the SOFC at time t. SOFC These are the conversion factors; The hydrogen energy derivative unit utilizes hydrogen and nitrogen generated by SOEC electrolysis to complete the ammonia synthesis reaction through an electro-ammonia conversion device, and then combines with carbon dioxide to complete the methanation process. The chemical reaction process is as follows: N2 + 3H2 → 2NH3; CO2 + 4H2 → CH4 + 2H2O; The ammonia synthesis rate is limited by pressure and temperature, showing a negative correlation with temperature and a positive correlation with pressure. The ammonia synthesis rate and the ammonia fuel cell model are as follows: In the formula, ν is the ammonia synthesis rate; P is the pressure of each gas; ν is the correction factor, taken as 4.75; ρ is the density of the ammonia synthesis catalyst; k1, k -1 T is the pre-exponential factor; r Room temperature; Power generation for ammonia fuel cells; Let t be the SOFC ammonia consumption power. The high calorific value of ammonia; Considering the energy consumption, hydrogen consumption, and methane formation rate during methanation, a methanation model is constructed based on the molar relationships of the chemical reaction process as follows: In the formula, These represent the methane production and hydrogen consumption during the methanation process, respectively. This represents the amount of methane consumed during the methanation process; These represent the thermal energy output and electrical power consumption during the methanation process, respectively. These are the efficiency of thermal conversion and electro-gas conversion, respectively. This is the calorific value of methane.

4. The multi-energy system security response collaborative defense method based on voltage sensitivity identification according to claim 1, characterized in that, In step one, the transport carrier module is specifically as follows: ∑ i∈N x i,k (t)≤1; z m (t)=1-∑ i∈N x i,k ; In the formula, x i,k This represents the connection status of the k-th mobile energy storage unit at node i. A value of 1 indicates that the mobile energy storage unit is connected to node i, and a value of 0 indicates that the mobile energy storage unit is not connected to node i. z represents the maximum number of mobile energy storage units that node i can connect to; m (t) represents the driving status of the mobile energy storage unit, with a value of 1 indicating that the mobile energy storage unit is driving on the road; T ij The travel time of the mobile energy storage unit from node i to node j.

5. The multi-energy system security response collaborative defense method based on voltage sensitivity identification according to claim 1, characterized in that, In step two, the voltage sensitivity calculation based on the median segmentation method is as follows: In the formula, K ij,t Let be the sensitivity of the power change at node j to the voltage at node i at time t; U represents the conventional sensitivity of the power change at node j to the voltage at node i at time t. i,t and Let be the actual voltage at node i at time t and its expected value, respectively. Considering the impact of wind and solar fluctuations and abnormal load behavior on the system under extremely cold weather, this study investigates the degree to which changes in the power of a certain node affect the voltage deviations of other nodes in the system. A median segmentation method is introduced, and the median of the sensitivity matrix columns is selected as the criterion to determine the sensitivity of node j to the entire system. To improve the accuracy of mobile energy storage unit access nodes, the sensitivity calculation method is as follows: In the formula, The median of the j-th column in the sensitivity matrix at time t; The sum of sensitivities in the sensitivity matrix at time t that are above the median. The sum of sensitivities in the j-th column of the sensitivity matrix at time t that is below the median. Let be the sensitivity of node j to the voltage of the entire system.

6. The multi-energy system security response collaborative defense method based on voltage sensitivity identification according to claim 5, characterized in that, In step two, during the source-grid-load three-sided risk sensitivity sensing, the source-side risk voltage sensitivity index K... source The calculation method is as follows: In the formula, Let t be the power of wind and solar power under limited load. λ represents the wind and solar power on a typical day at time t; source,t λ is the risk weighting coefficient for the source measurement period t. When the wind and solar loss rate is 0, λ source,t =1 indicates that the impact of the risk scenario on the source side at time t is 0; Grid-side risk voltage sensitivity index K grid as follows: L I,t =9.8×10 -3 r i π(D+d t )d t ; In the formula, d t The cumulative icing thickness at time t; ΔT is 15 min; ρ i r is the density of ice. r For rainfall rate; ρ w The density of water; v t Let L be the wind speed at time t; I,t and L W,t Here, represents the ice load and wind load per unit length of the line at time t; D is the line diameter, which is 10 mm; E is the span factor; F... t P represents the icing wind load per unit length of the line at time t; b,t Let a be the probability of failure per unit length of line during time period t; IW and b IW These are the ice wind load threshold values ​​for normal operation and fault confidence level of the distribution line, respectively; A cut,t Let A be the number of branches with the probability of a line fault at time t; l λ represents the total number of branches; grid,t For network-side time period t, the risk weighting coefficient is used. When the probability of line breakage is 0, λ grid,t =1 indicates that the impact of the risk scenario on the network side at time t is 0; Load-side risk voltage sensitivity index K load as follows: In the formula, Let t be the amount of load loss; Let t be the rated load power; λ load,t λ is the risk weighting coefficient at time t on the load side; when the load loss rate is 0, λ load,t A value of 1 indicates that the impact of the risk scenario on the load side at time t is 0; In step two, the identification of weak points and the determination of the location for connecting mobile energy storage are as follows: In the formula, The risk weighting coefficients for the source-grid-load three sides are assigned using a composite weighting method.

7. The multi-energy system security response collaborative defense method based on voltage sensitivity identification according to claim 1, characterized in that, In step three, the safety indicator Ψ1 and the risk-economic indicator Ψ2 are as follows: In the formula, U i,t U is the square of the actual voltage value of node i at time t. i,min and U i,max U is the square of the minimum and maximum voltage values ​​at node i at time t. i,N The square of the rated voltage at node i; P represents the load loss at time t. MESS,k,t p represents the active power of the k-th mobile energy storage unit at time t. lt ψ2 represents the cost of load loss; K represents the number of mobile energy storage units; a smaller ψ2 indicates a stronger load guarantee capability.

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