Power distribution network toughness evaluation method based on mobile energy storage device

Abstract

The invention discloses a power distribution network toughness evaluation method based on a mobile energy storage device, and belongs to the technical field of power distribution network toughness evaluation. The distribution line fault rate under the combined extreme weather of strong wind, thunderstorm and the like is calculated, then the toughness index of the power distribution network is calculated, and the power distribution network toughness evaluation process is adopted for evaluation so as to improve and optimize the disaster resistance toughness of the power distribution network. According to the toughness evaluation method, more extreme weathers are considered, and the calculation result is closer to reality.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network resilience assessment, and in particular relates to a distribution network resilience assessment method based on a mobile energy storage device. Background Art

[0002] The distribution network is an important part of the smart grid. Different from the single scenario of power transmission and transformation, the terminal access of the distribution network is complex, the probability of failure is high, there are many risk factors, and the problems of power quality cannot be well solved. With the further implementation of the "dual carbon" policy, the application of a large number of distributed energy generation and new energy appliances requires a good access environment for the distribution network.

[0003] The development of smart distribution networks is an inevitable requirement for the development of the power industry. The reliability assessment of distribution networks is part of the informatization and intelligence of distribution networks. Understanding the reliability of distribution networks is conducive to the formulation of operation and maintenance plans for distribution networks; it is also conducive to the arrangement of defense strategies and the transportation of disaster materials under disaster weather conditions. Through the access of renewable energy and distributed generation energy, power users can participate in the interaction of power grids, and cooperate with the monitoring, control, analysis, evaluation, early warning, optimization and self-healing of distribution networks to enhance the resilience of distribution networks against disasters. At present, grid reinforcement is considered to be one of the most effective measures to improve the resilience of distribution networks, including measures such as cabling of overhead lines and enhancing the disaster resistance of towers, but the investment cost required for the above measures is too high to be widely promoted. Therefore, how to formulate economical and effective disaster prevention and disaster resistance plans under the condition of limited investment budget for grid planning has become an urgent problem to be solved in the planning of resilient distribution networks. The development of energy storage technology provides an important idea and way to solve the above problems. With the continuous maturity of battery technology and the continuous reduction of costs, the application of energy storage in power systems is accelerating. Energy storage has the advantages of flexible capacity configuration, fast response speed, and stable output power. It is mainly used in power systems for peak load regulation, frequency regulation, congestion relief, voltage support, reactive power control, and emergency standby. It can provide valuable services such as reducing asset investment, strengthening power system stability, and absorbing new energy. It can basically be applied to all stages of source, network, and load. In addition, compared with single line reinforcement and transformation measures, the island operation capability of energy storage can ensure the power supply security of important loads far away from the upper power source, but the idea of ​​improving the resilience of the distribution network does not take into account the impact of extreme weather. Summary of the invention

[0004] This patent proposes a distribution network resilience assessment method based on a mobile energy storage device. By introducing the impact of various extreme weather conditions such as strong winds and thunderstorms on distribution lines, the line failure rates are calculated separately, and a failure rate model of the distribution network under combined extreme weather conditions is proposed. A distribution network resilience index is established, and a distribution network resilience assessment method is proposed based on the above content, which effectively reduces the investment cost of disaster prevention and mitigation of the power grid.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A distribution network resilience assessment method based on a mobile energy storage device comprises the following steps:

[0007] Step 1: Obtain relevant parameters of the power grid network structure and weather data;

[0008] Step 2: Build a failure rate model;

[0009] Step 3: Compare the failure rate of each component in the distribution network with its limit value at regular intervals to determine whether to trigger pre-dispatch; if the current position to be dispatched coincides with the previous dispatched position, it is determined that the dispatch will not be performed at present;

[0010] Step 4: Check whether all the stored mobile energy storage devices have been dispatched. If the number of stored mobile energy storage devices is 0, no pre-dispatch is performed. Otherwise, determine whether extreme weather has occurred. If so, proceed to the next step. Otherwise, return to step 3.

[0011] Step 5: When extreme weather occurs, the working status of each component is determined at intervals. Once a component fails, a rescue dispatch strategy based on a mobile energy storage device is immediately executed;

[0012] Step 6: After pre-dispatching before extreme weather and real-time dispatching during the disaster process until the restoration of normal functions, the resilience index TGH of the entire distribution network is calculated to evaluate its resilience, and optimization and rectification are carried out based on the dispatch results.

[0013] Furthermore, in step 1, the parameters and data obtained include the tensile strength of the conductor, the projected width of the overhead conductor, the density of ground lightning, the single-sided lightning induction range of the line, the lightning resistance level, the distance from the line to the lightning strike site, the duration after the distribution network fault occurs, the power load of the distribution network under normal circumstances, and the power load of the distribution network after adopting the mobile energy storage strategy.

[0014] Furthermore, in step 2, it is assumed that the tensile strength of the conductor follows a normal distribution, and the failure rate P1 of the distribution line under strong wind is calculated:

[0015]

[0016] In the formula, f(δ r ) represents the probability density function, δ r Indicates the tensile strength of the wire, r r represents the standard deviation of tensile strength, μ r It represents the mean value of tensile strength;

[0017] The wind load is calculated from the wind speed, and the failure rate of the distribution line under strong wind is:

[0018]

[0019] Where v represents the wind load.

[0020] Furthermore, in step 2, the failure rate of the distribution line under the action of thunderstorm is calculated. The impact of thunderstorm weather on the distribution line is characterized by the failure rate P2 of the line under lightning strike. Under normal conditions, the line tripping rate in a certain area can be written as:

[0021]

[0022]

[0023]

[0024] In the formula, c e Indicates the projection width of the overhead wire, G n represents the density of ground-to-ground lightning, B e Indicates the single-side lightning range of the line, I e It indicates the lightning resistance level, s indicates the distance from the line to the lightning strike location, Indicates the direct lightning trip rate, Indicates the induced lightning trip rate, represents the line tripping rate, η represents the arcing rate;

[0025] The failure rate P2 and trip rate of the line under lightning strike can be obtained by linear function fitting. The relationship κ.

[0026]

[0027] Furthermore, in step 2, the tripping rate under lightning strike and the line tensile failure rate under strong wind are comprehensively considered, and the failure rate of the distribution line under combined extreme weather conditions such as strong wind and thunderstorm is P.

[0028]

[0029] Furthermore, in step 6, the specific expression of the toughness index TGH is:

[0030]

[0031] Where, T p represents the duration after the distribution network fails, K represents the set of all disaster events (including strong winds and lightning strikes), p n_t represents the power load of the distribution network under normal circumstances, p ME_t Represents the power load of the distribution network after adopting the mobile energy storage strategy.

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

[0033] 1) Reduce the scale of line transformation and reduce investment costs;

[0034] 2) Improve the disaster prevention and mitigation capabilities of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of the distribution network resilience assessment method based on the mobile energy storage device of the present invention;

[0036] Figure 2 The load variation curve diagram of the power grid provided by the present invention;

[0037] Figure 3 This is a structural diagram of a power distribution network in a certain area in the present invention. DETAILED DESCRIPTION

[0038] In order to better understand the present invention, the content of the present invention is further clearly set forth in conjunction with the embodiments below, but the protection content of the present invention is not limited to the following embodiments. In the following description, a large number of specific details are provided in order to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details.

[0039] Example

[0040] See also Figure 1-3 , a distribution network resilience assessment method based on a mobile energy storage device. To ensure the accuracy of the assessment results, at least two extreme weather conditions need to be considered. By adding the impact of multiple extreme weather conditions such as strong winds and thunderstorms on the distribution lines, the line failure rates are calculated separately, a failure rate model of the distribution network under combined extreme weather conditions is proposed, a distribution network resilience index is established, and a distribution network resilience assessment method is proposed based on the above content. In an embodiment of the present invention, the distribution network structure diagram is derived from an actual coastal area in an actual region, and the data processing is completed using the numerical calculation software MATLAB. The technical solution of the present invention is specifically described as follows:

[0041] (1) Taking a certain wind disaster process as an example, set the failure rate limit and obtain relevant data such as the wind speed of the wind disaster, the location of the thunderstorm, and the lightning range.

[0042] (2) Assuming that the tensile strength of the conductor follows a normal distribution, the failure rate P1 of the distribution line under strong wind is calculated as follows:

[0043]

[0044] In the formula, f(δ r ) represents the probability density function, δ rIndicates the tensile strength of the wire, r r represents the standard deviation of tensile strength, μ r Represents the mean tensile strength.

[0045] The wind load is calculated from the wind speed, and the failure rate of the distribution line under strong wind is:

[0046]

[0047] In the formula, v represents wind load. The process of calculating wind load by wind speed refers to "DL / T 5551-2018. Specification for Loads of Overhead Transmission Lines [S]. Beijing: China Planning Press, 2018."

[0048] (3) Calculate the failure rate of distribution lines under thunderstorms. The impact of thunderstorms on distribution lines is represented by the failure rate P2 of the lines under lightning strikes. Under normal conditions, the line tripping rate in a certain area can be written as:

[0049]

[0050]

[0051]

[0052] In the formula, c e Indicates the projection width of the overhead wire, G n represents the density of ground-to-ground lightning, B e Indicates the single-side lightning range of the line, I e It indicates the lightning resistance level, s indicates the distance from the line to the lightning strike location, Indicates the direct lightning trip rate, Indicates the induced lightning trip rate, The line trip rate is represented by η, and the arc building rate is represented by η. The failure rate P2 and trip rate of the line under lightning strike can be obtained by linear function fitting. The relationship k, lightning failure rate is:

[0053]

[0054] (4) Taking into account the tripping rate under lightning strike and the line tensile failure rate under strong wind, the failure rate of distribution lines under combined extreme weather conditions such as strong wind and thunderstorm is P.

[0055]

[0056] (5) Calculate the toughness index TGH using the toughness evaluation method.

[0057] In order to quantitatively describe the degree to which the mobile energy storage strategy improves resilience, a resilience index TGH is proposed. This index is defined based on the ratio of the disaster process and the corresponding time history in the disaster occurrence and recovery stages. The smaller the index, the shorter the required recovery time, that is, the higher the resilience of the distribution network. The specific expression is:

[0058]

[0059] Where, T p represents the duration after the distribution network fails, K represents the set of all disaster events (including strong winds and lightning strikes), p n_t represents the power load of the distribution network under normal circumstances, p ME_t Represents the power load of the distribution network after adopting the mobile energy storage strategy.

[0060] Based on the above steps, the distribution network resilience assessment process is as follows:

[0061] (1) Input the relevant parameters of the distribution network structure and weather data into the failure rate model;

[0062] (2) Calculate the line failure rate distribution based on the distribution network failure rate model under extreme weather conditions;

[0063] (3) Compare the failure rate of each component in the distribution network with its limit value at regular intervals to decide whether to trigger pre-dispatch. If the location where the mobile energy storage device needs to be dispatched is the same as the location where it was last determined to be dispatched, no new one will be dispatched.

[0064] (4) Check whether all the stored mobile energy storage devices have been dispatched. If the number of stored mobile energy storage devices is 0, do not perform pre-dispatch. Otherwise, determine whether extreme weather has occurred. If so, proceed to the next step. Otherwise, return to step (3).

[0065] (5) When extreme weather occurs, the working status of each component is determined at intervals. Once a component fails, a rescue dispatch strategy based on mobile energy storage devices is immediately implemented;

[0066] (6) After pre-dispatching before extreme weather and real-time dispatching during the disaster process until the restoration of normal functions, the resilience index TGH of the entire distribution network is calculated to evaluate its resilience, and optimization and rectification are carried out based on the dispatch results.

[0067] Finally, it should be noted that the above embodiments are only preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention rather than to limit it. Any modification, improvement, other modification or equivalent replacement made to the technical solution of the present invention by ordinary technicians in this field, as long as they do not depart from the spirit and scope of the technical solution of the present invention, should be included in the protection scope of the present invention.

Claims

1. A distribution network resilience assessment method based on a mobile energy storage device, characterized in that: The following steps are involved: Step 1: Obtain relevant parameters of the power grid network structure and weather data; Step 2: Build a failure rate model; Step 3: Compare the failure rate of each component in the distribution network with its limit value at regular intervals to determine whether to trigger pre-dispatch; if the current position to be dispatched coincides with the previous dispatched position, it is determined that the dispatch will not be performed at present; Step 4: Check whether all the stored mobile energy storage devices have been dispatched. If the number of stored mobile energy storage devices is 0, no pre-dispatch is performed. Otherwise, determine whether extreme weather has occurred. If so, proceed to the next step. Otherwise, return to step 3. Step 5: When extreme weather occurs, the working status of each component is determined at intervals. Once a component fails, a rescue dispatch strategy based on a mobile energy storage device is immediately executed; Step 6: After pre-dispatching before extreme weather and real-time dispatching during the disaster process until the restoration of normal functions, the resilience index TGH of the entire distribution network is calculated to evaluate its resilience, and optimization and rectification are carried out based on the dispatch results.

2. The distribution network resilience assessment method based on a mobile energy storage device according to claim 1 is characterized in that: In step 1, the parameters and data obtained include the tensile strength of the conductor, the projection width of the overhead conductor, the wind speed, the density of ground lightning, the single-sided lightning induction range of the line, the lightning resistance level, the distance from the line to the lightning strike site, the duration after the distribution network fault occurs, the power load of the distribution network under normal circumstances, and the power load of the distribution network after the mobile energy storage strategy is adopted.

3. The distribution network resilience assessment method based on a mobile energy storage device according to claim 1 is characterized in that: In step 2, it is assumed that the tensile strength of the conductor follows a normal distribution, and the failure rate P1 of the distribution line under strong wind is calculated: In the formula, f(δ r ) represents the probability density function, δ r Indicates the tensile strength of the wire, r r represents the standard deviation of tensile strength, μ r It represents the mean value of tensile strength; The wind load is calculated from the wind speed, and the failure rate of the distribution line under strong wind is: P1=1-∫ v +∞ f(d r )dδ r (2) Where v represents the wind load.

4. The distribution network resilience assessment method based on a mobile energy storage device according to claim 3 is characterized in that: In step 2, the failure rate of the distribution line under thunderstorm is calculated. The impact of thunderstorm on the distribution line is characterized by the failure rate P2 of the line under lightning strike. Under normal conditions, the line tripping rate in a certain area can be written as: In the formula, c e Indicates the projection width of the overhead wire, G n represents the density of ground-to-ground lightning, B e Indicates the single-side lightning range of the line, I e It indicates the lightning resistance level, s indicates the distance from the line to the lightning strike location, Indicates the direct lightning trip rate, Indicates the induced lightning trip rate, represents the line tripping rate, η represents the arcing rate; The failure rate P2 and trip rate of the line under lightning strike can be obtained by linear function fitting. The relationship κ.

5. The distribution network resilience assessment method based on a mobile energy storage device according to claim 4 is characterized in that: In step 2, the tripping rate under lightning strike and the line tensile failure rate under strong wind are comprehensively considered, and the failure rate of the distribution line under combined extreme weather such as strong wind and thunderstorm is P.

6. The distribution network resilience assessment method based on a mobile energy storage device according to claim 1 is characterized in that: In step 6, the specific expression of the toughness index TGH is: Where, T p represents the duration after the distribution network fails, K represents the set of all disaster events (including strong winds and lightning strikes), p n_t represents the power load of the distribution network under normal circumstances, p ME_t Represents the power load of the distribution network after adopting the mobile energy storage strategy.

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