Power distribution network new energy credible capacity evaluation method considering uncertainty

Abstract

A power distribution network new energy credible capacity evaluation method considering new energy power generation uncertainty comprises the following steps: respectively establishing corresponding output models for photovoltaic and wind power new energy, collecting power distribution network topology and new energy output historical data for fitting, and calculating an original power distribution network reliability index based on a processing model; accessing the new energy unit model into the output model, and calculating a reliability index R0 through a sequence Monte Carlo method; replacing the new energy unit model with the conventional unit model, and calculating a reliability index R1 accessed to the conventional unit according to the capacity initial value; and calculating the capacity of the conventional unit to be accessed through a secant method until the difference value between the reliability indexes R0 and R1 is smaller than a given value, obtaining the credible capacity of the new energy unit corresponding to the conventional unit, and calculating the capacity credibility. According to the method, the uncertainty of new energy is considered, corresponding output models are respectively established for photovoltaic and wind power new energy, the credible capacity of the new energy is measured from a power generation side, the credible capacity is searched by using a secant method, and evaluation of the credible capacity of the power distribution network is completed.

Description

Technical Field

[0001] The present invention relates to a technology in the field of distribution networks, specifically a method for evaluating the credible capacity of new energy in a distribution network considering the uncertainty of new energy power generation. Background Art

[0002] Existing distribution network capacity estimation technologies only involve one of the new energy power generations, namely wind power or photovoltaic power. Moreover, when using the bisection method to iteratively search for the access capacity of conventional units in the calculation of the credible capacity stage, the iteration speed is relatively slow. Summary of the Invention

[0003] In view of the above defects and deficiencies of the prior art, the present invention proposes a method for evaluating the credible capacity of new energy in a distribution network considering the uncertainty of new energy power generation. Considering the uncertainty of new energy, corresponding output models are established for photovoltaic and wind power respectively. Starting from the power generation side, the credible capacity of new energy is measured, and the secant method is used to search for the credible capacity to complete the evaluation of the credible capacity of the distribution network.

[0004] The present invention is realized through the following technical solutions:

[0005] The present invention relates to a method for evaluating the credible capacity of new energy in a distribution network considering the uncertainty of new energy power generation, including:

[0006] Step 1) Establish corresponding output models for photovoltaic and wind power respectively, collect the topology of the distribution network and historical data of new energy output for fitting, and calculate the original distribution network reliability index based on the processing model;

[0007] Step 2) Connect the new energy unit model to the output model, and calculate the reliability index R0 through the sequential Monte Carlo method; then replace the new energy unit model with the conventional unit model, and calculate the reliability index R1 for connecting the conventional unit according to the initial capacity value;

[0008] Step 3) Calculate the capacity of the conventional unit to be connected through the secant method. Until the difference between the reliability indexes R0 and R1 is less than a given value, the credible capacity of the new energy unit corresponding to the conventional unit is obtained and the capacity credibility is calculated.

[0009] The output model includes: a wind speed model, a wind speed-power characteristic model for wind power generation, a light intensity model, and a light-power characteristic model for photovoltaic power generation.

[0010] The wind speed model is simulated by the Weibull distribution, specifically: Where: ν represents the wind speed, k and c are the parameters constituting the Weibull distribution, μ 1 is the mean value of the wind speed, and σ 1 is the standard deviation of the wind speed.

[0011] The wind speed-power characteristic model of wind power generation is as follows: where: v ci is the minimum wind speed required to start wind power generation, i.e., the cut-in wind speed, v cr is the rated wind speed, v co is the maximum wind speed that the wind power generation equipment can withstand, i.e., the cut-out wind speed. P wtr is the rated power of wind power generation.

[0012] The described light intensity model follows the beta distribution, specifically: where: S represents the light intensity, S max is the maximum value of the light intensity. Since the Beta function is defined on [0,1], the light intensity data is normalized to the interval [0,1]. Г(x) is the Gamma function, and α and β are the two parameters that make up the function, μ 2 is the mean value of the light intensity, σ 2 is the variance of the light intensity.

[0013] The light intensity-power characteristic model of photovoltaic power generation is as follows: where: S r is the rated light intensity, P pvr is the rated power of photovoltaic power generation.

[0014] The described reliability indicators include:

[0015] 1) System average interruption frequency SAIFI, that is, the average number of power outages per user within a specified time (usually one year), unit times / (household·a): where: R represents the set of load nodes, N i represents the number of users corresponding to the i-th load, λ i represents the number of power outages corresponding to the i-th load.

[0016] 2) System average interruption duration SAIDI, that is, the average power outage time per user within a specified time, unit h / (household·a): where: U i represents the power outage time corresponding to the i-th load.

[0017] 3) Customer average interruption duration CAIDI, that is, the average power outage time per user per power outage within a specified time, unit h / time:

[0018] 4) Expected energy not supplied EENS, that is, the total shortfall of electricity that cannot be supplied due to forced outages within a specified time, unit kW·h / a: where: Pi,load It refers to the active power demand of the i-th load.

[0019] 5) Average power supply availability ASAI, that is, the time ratio of the user's load demand being met within a specified time:

[0020] The present invention relates to a system for implementing the above method, including: a new energy output unit, a distribution network reliability assessment unit, and a new energy generation credible capacity assessment unit. Among them: the new energy output unit fits the wind speed and light intensity respectively using the Weibull distribution and the beta distribution according to historical data, and obtains the output from the power characteristics; the distribution network reliability assessment unit determines the faulty components according to the distribution network topology and component reliability parameter information, and analyzes and calculates the reliability indexes of the system according to the new energy output situation; the credible capacity assessment unit calculates the reliability indexes when connecting new energy units according to the distribution network topology, load, and new energy output situation information, then calculates the reliability indexes after removing the new energy units and connecting conventional units, and adjusts the capacity of the conventional units until the reliability indexes are the same, so as to obtain the credible capacity of the new energy units corresponding to the conventional units at this time. Technical effects

[0021] The present invention uses the secant method to calculate the capacity of the connected conventional units, which can have a faster convergence speed and simpler modeling difficulty during iterative calculation. Description of the drawings

[0022] Figure 1 It is a flow chart for calculating the credible capacity of new energy units.

[0023] Figure 2 It is the topology diagram of the IEEE 33-node distribution network.

[0024] Figure 3 It is the wind speed-power characteristic curve of the wind turbine.

[0025] Figure 4 It is the light-power characteristic curve of the photovoltaic unit.

[0026] Figure 5 It is a flow chart for calculating the reliability indexes of the distribution network by the sequential Monte Carlo method.

[0027] Figure 6 It is a schematic diagram of searching for the access capacity of conventional units by the secant method.

[0028] Figure 7 It is the comparison of the power outage time of each node before and after connecting new energy units. Detailed implementation manners

[0029] Such as Figure 1The present embodiment relates to a method for evaluating the credible capacity of new energy in a distribution network considering the uncertainty of new energy power generation, including the following steps:

[0030] 1) Establish output models for wind power generation and photovoltaic power generation, and after fitting based on historical data; according to the distribution network topology, required load, and parameters of new energy distributed power sources, calculate the original reliability index of the distribution network when no new energy units are connected.

[0031] 2) Based on the sequential Monte Carlo method, calculate the reliability index of the distribution network after connecting new energy units considering the uncertainty of new energy on the basis of the output model in step 1.

[0032] 3) Set the initial value of the capacity of the conventional unit connected, and calculate the reliability index of the distribution network when the conventional unit is connected and no new energy unit is connected on the basis of the output model in step 1.

[0033] 4) Based on the original reliability index and the reliability index of connecting the initial value of the conventional unit, calculate the capacity of the conventional unit to be connected in the next iteration on the basis of the output model in step 1 through the secant method.

[0034] 5) When the reliability index corresponding to connecting the conventional unit is approximately the same as the reliability index when connecting the new energy unit, obtain the credible capacity corresponding to the new energy unit, and the capacity credibility is the credible capacity divided by the total capacity of the unit connected.

[0035] As Figure 3 shown, it is the wind speed-power characteristic curve of the wind turbine in the output model.

[0036] As Figure 4 shown, it is the light intensity-power characteristic of the photovoltaic unit in the output model.

[0037] As Figure 5 shown, the sequential Monte Carlo method specifically includes:

[0038] 1) Establish a corresponding line-load fault impact matrix according to the topology of the distribution network to determine whether the line fails and thus determine the switching of the load on this line.

[0039] 2) Sample to determine the normal working time t 1 and fault repair time t 2 of each component, and then determine the faulty component, where: λ is the failure rate of the component, μ is the repair rate of the component, and γ is a random number uniformly distributed on [0,1].

[0040] 3) Determine the output size of the unit according to the simulated time, analyze the affected load in this simulation, and calculate the reliability index.

[0041] 4) After repeating the simulation until the maximum simulation time, the reliability indices of each node are statistically analyzed, and the reliability index of the distribution network is calculated.

[0042] The component reliability mentioned above includes:

[0043] 1) Failure rate, which is the probability that a component fails within a unit time, denoted by λ.

[0044] 2) Mean time to failure (MTTF), which is the average time that a component can work normally. It is generally considered that the time to failure follows an exponential distribution. In this case, the failure rate λ is a constant, and the mean time to failure and the failure rate are reciprocal to each other.

[0045] 3) Repair rate, which is the probability that a component is successfully repaired within a unit time, denoted by μ.

[0046] 4) Mean time to repair (MTTR), which is the average time required to repair a component. It is generally considered that the repair time follows an exponential distribution. In this case, the repair rate μ is a constant, and the mean time to repair and the repair rate are reciprocal to each other.

[0047] 5) Forced outage rate, which is the probability that a component experiences a forced outage during the corresponding time, and is also the ratio of the failure time to the total time.

[0048] The secant method mentioned above means: Taking R 0 as the reliability index of the distribution network after connecting new energy units, C C as the point P on the ideal credible capacity corresponding function, the original unit corresponding point is A, the reliability index is R, and the connected C k capacity corresponding point, the reliability index is R k , connecting points A and B, intersecting with the reliability index R 0 horizontal axis at point C, calculating the reliability index of the unit capacity C k+1 under point C, that is, the difference between the ordinate of point D and R 0 , that is, whether the distance of CD meets the requirements. If it meets the requirements, the credible capacity is obtained; otherwise, point D is selected as the next iteration point to continue the secant method search until the requirements are met.

[0049] The reliability index R 0 horizontal axis, that is, the reliability index of the distribution network after connecting new energy units.

[0050] The iteration point is obtained through the following method: Where: x k+1 is the approximate root of the (k + 1)-th iteration, x k and f(x k) are the approximate root and function value of the k-th iteration, x k-1 and f(x k-1 ) are the approximate root and function value of the (k - 1)-th iteration respectively.

[0051] After specific actual experiments, in the scenario of using the IEEE 33-node distribution network, the system includes 32 load nodes, and the corresponding load peak is set to 6685 kW. Photovoltaic units with a capacity of 800 kW and wind turbines with a capacity of 1000 kW are connected to node 8 and node 24 respectively. The annual output of the new energy distributed power source can be obtained by fitting. The distribution network reliability indexes before and after connecting the new energy units are calculated by the sequential Monte Carlo method respectively. The simulation time length is set to 100 years, and the reliability indexes are specified with one year as the specified time. The comparison of the power outage time of each node before and after connecting the new energy units is as Figure 7 shown. The comparison of the system reliability indexes before and after connecting the new energy units is shown in Table 1.

[0052] Table 1 Comparison of System Reliability Before and After Connecting New Energy Units

[0053] Compared with the prior art, by using the secant method in the process of searching for the access capacity of conventional units, the present method significantly improves the iteration speed and has a lower modeling difficulty.

[0054] The above specific implementation can be locally adjusted by those skilled in the art in different ways without departing from the principles and purposes of the present invention. The protection scope of the present invention is subject to the claims and is not limited by the above specific implementation. All implementation solutions within its scope are subject to the present invention.

Claims

1. A method for evaluating the credible capacity of new energy in a distribution network considering the uncertainty of new energy power generation, characterized in that, it includes: Step 1) Establish corresponding output models for two types of new energy, namely photovoltaic and wind power. Collect the topology of the distribution network and historical data of new energy output for fitting, and calculate the original distribution network reliability index based on the processing model; Step 2) Connect the new energy unit model to the output model, and calculate the reliability index R0 through the sequential Monte Carlo method; then replace the new energy unit model with the conventional unit model, and calculate the reliability index R1 of connecting the conventional unit according to the initial capacity value; Step 3) Calculate the capacity of the conventional unit to be connected through the secant method. Until the difference between the reliability indexes R0 and R1 is less than the given value, obtain the credible capacity of the new energy unit corresponding to the conventional unit and calculate the capacity credibility; The output model includes: a wind speed model, a wind speed-power characteristic model of wind power generation, a light intensity model, and a light-power characteristic model of photovoltaic power generation.

2. The method for evaluating the credible capacity of new energy in a distribution network considering the uncertainty of new energy power generation according to claim 1, characterized in that, The described wind speed model is simulated by Weibull distribution, specifically as follows: where: ν represents the wind speed, and k and c are the parameters constituting the Weibull distribution, μ 1 is the mean value of the wind speed, and σ 1 is the standard deviation of the wind speed.

3. The method for evaluating the credible capacity of new energy in a distribution network considering the uncertainty of new energy power generation according to claim 1, characterized in that, The wind speed-power characteristic model of wind power generation is as follows: Where: v ci is the minimum wind speed required to start wind power generation, i.e., the cut-in wind speed, v cr is the rated wind speed, v co is the maximum wind speed that the wind power generation equipment can withstand, i.e., the cut-out wind speed, P wtr is the rated power of wind power generation.

4. The method for evaluating the credible capacity of new energy in a distribution network considering the uncertainty of new energy power generation according to claim 1, characterized in that, The described light intensity model follows a beta distribution, specifically: where: S represents the light intensity, S max is the maximum value of the light intensity. Since the Beta function is defined on [0,1], the light intensity data is normalized to the interval [0,1]. Г(x) is the Gamma function, and α and β are the two parameters that make up the function, μ 2 is the mean value of the light intensity, and σ 2 is the variance of the light intensity.

5. The method for evaluating the credible capacity of new energy in a distribution network considering the uncertainty of new energy power generation according to claim 1, characterized in that, The light-power characteristic model of the photovoltaic power generation is as follows: Where: S r is the rated light intensity, and P pvr is the rated power of the photovoltaic power generation.

6. The method for evaluating the credible capacity of new energy in a distribution network considering the uncertainty of new energy power generation according to claim 1, characterized in that, The reliability index includes: 1) System Average Interruption Frequency Index (SAIFI), that is, the average number of power outages per user within a specified time (usually one year), with the unit of times / (household·year): Where: R represents the set of load nodes, N i represents the number of users corresponding to the i-th load, and λ i represents the number of power outages corresponding to the i-th load; 2) System Average Interruption Duration Index (SAIDI), that is, the average interruption time of each user within a specified time, with the unit of h / (household·year): Where: U i represents the interruption time corresponding to the i-th load; 3) Customer Average Interruption Duration Index (CAIDI), which is the average interruption time per customer per interruption within a specified time, with the unit of h / interruption: 4) Expected Energy Not Supplied (EENS), which is the total shortfall in power that cannot be supplied due to forced outages within a specified period, with the unit of kW·h / a: where: P i,load refers to the active power required for the i-th load; 5) Average Supply Availability Index (ASAI), which is the proportion of time when the user's load demand is met within a specified period:

7. A system for evaluating the credible capacity of new energy in a distribution network considering the uncertainty of new energy power generation for implementing any of the methods described in claims 1-6, characterized in that, it includes: A new energy output unit, a distribution network reliability evaluation unit, and a new energy power generation credible capacity evaluation unit, where: the new energy output unit fits the wind speed and light intensity respectively using the Weibull distribution and the beta distribution according to historical data, and obtains the output from the power characteristics; the distribution network reliability evaluation unit determines the faulty components according to the distribution network topology and component reliability parameter information, and analyzes and calculates the reliability index of the system according to the new energy output situation; The credible capacity evaluation unit calculates the reliability index when connecting the new energy unit according to the distribution network topology, load, and new energy output situation information, then calculates the reliability index after removing the new energy unit and connecting the conventional unit, and adjusts the capacity of the conventional unit until the reliability indexes are the same, and obtains the credible capacity of the new energy unit corresponding to the conventional unit at this time.

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