A vulnerability assessment method for multi-energy distribution networks

Abstract

This invention relates to a vulnerability assessment method for multi-energy distribution networks. The method includes setting initial data, sampling wind speed using a distributed generation (DG) output model, calculating DG output and performing power flow calculations to obtain DG output control parameters, and simultaneously implementing coordinated control measures for DG output to achieve coordinated control of multiple energy sources. Afterwards, it determines whether sampling is complete; if complete, vulnerability indicators are calculated and the assessment ends; if incomplete, the method returns to re-sampling to obtain wind speed. This invention conducts in-depth research on the stochasticity of DG output and output control strategies in distribution networks, deriving an overall process for vulnerability assessment of multi-energy distribution networks. This reduces the predictability and unpredictability risks of distribution networks, providing a prerequisite for the safe and stable operation of distribution networks.

Description

Technical Field

[0001] This invention belongs to the field of power grid evaluation, and relates to distribution evaluation technology, especially a vulnerability assessment method for multi-energy distribution networks. Background Technology

[0002] Distribution networks are systems directly connected to users. Compared to transmission networks, distribution networks are characterized by higher impedance ratios and more complex circuit structures. Furthermore, small- to medium-capacity distributed power sources rarely participate directly in voltage regulation after being connected to the distribution network. Therefore, it is necessary to predict and analyze the vulnerability indicators of the distribution network, restructure the distribution network, and effectively enable its self-recovery, thereby improving the security and reliability of the distribution network.

[0003] The integration of distributed generation (DG) improves power supply reliability but also alters the distribution network structure and power flow, transforming the traditional radial distribution network into a multi-source system, thus posing certain risks to its operation and control. Therefore, in assessing the reliability, risk, and vulnerability of distribution networks, DG cannot be equated entirely with traditional backup power sources, making existing vulnerability assessment methods insufficient for current operational needs. Current analytical, simulation, and hybrid methods combining analytical and simulation approaches for distribution network vulnerability assessment are limited by the complexity of real-world distribution networks and lack demonstrative applications, making them difficult to directly apply to practical distribution network risk assessments. Therefore, it is necessary to provide a vulnerability assessment method for multi-energy distribution networks under the new environment of DG. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a vulnerability assessment method for multi-energy distribution networks. This method fully considers the characteristics of random power output from distributed generation sources and studies vulnerability assessment methods for multi-energy distribution networks in new environments containing distributed generation sources. Based on defining practical vulnerability assessment indicators for distribution networks, this invention proposes equipment and network risk assessment models suitable for vulnerability assessment of multi-source distribution networks with complex network structures, which can effectively improve the risk resistance of future multi-source distribution networks.

[0005] The technical problem solved by this invention is achieved through the following technical solution:

[0006] A vulnerability assessment method for multi-energy distribution networks includes: setting up initial data, sampling wind speed using a distributed generation (DG) output model, calculating DG output and performing power flow calculations to obtain DG output control parameters, simultaneously implementing coordinated control measures for DG output, transmitting the operating status of each bus to the central controller via a communication link, calculating and feeding back the required output value to the DG via the communication link to achieve coordinated control of multiple energy sources, then determining whether sampling is complete; if complete, calculating vulnerability indicators and ending the assessment; if incomplete, returning to resample to obtain wind speed.

[0007] Furthermore, the method for establishing the distributed power output model is as follows:

[0008] 1) Photovoltaics

[0009] G=Gb×SIF+(1-cc)×Gd+cc×tau×(Gb+Gd)

[0010] Where Gb represents the beam illumination condition;

[0011] Gd represents diffuse irradiance;

[0012] SIF simulates the unstable behavior of clouds in response to irradiance, with values ​​of 0 or 1.

[0013]

[0014] Where Ts is the sampling time;

[0015] LPtau is the time constant of the low-pass filter;

[0016] LPyOld is a placeholder for a low-pass filter.

[0017] After calculating the solar radiation, the photovoltaic output can be determined.

[0018] pAvb = kPV × PF × Go

[0019] Where kPV is the conversion coefficient of light into electrical energy;

[0020] PF stands for power factor;

[0021] Go represents the solar radiation value;

[0022] 2) Fan

[0023]

[0024] Where V is the wind speed;

[0025] C is the scale parameter of the distribution, which reflects the average wind speed;

[0026] K is the shape parameter of the distribution.

[0027] PW = KWT × PF × V 3

[0028] Wherein, KWT is the conversion coefficient of wind speed into electrical energy;

[0029] PF stands for power factor.

[0030] The output of the gas turbine and capacitor bank can be adjusted according to actual needs, and its output value is relatively fixed during grid connection.

[0031] Furthermore, the distributed generation output is calculated using a distributed generation output model, and power flow calculations are performed to obtain the distributed generation output control parameters. The specific method is as follows:

[0032]

[0033] Where Yt represents a certain operating state of the system, including voltage and power flow.

[0034] Ei represents an accident that may occur at some point in the near future;

[0035] L represents the load at each busbar at time t;

[0036] P(Yt / E,L) is the probability distribution of the power grid operating state after Ei appears;

[0037] S(Yt) represents the severity of the impact on the system after the accident.

[0038] R(Yt / E,L) is the calculated vulnerability index value.

[0039] Furthermore, the vulnerability index definition includes low voltage vulnerability index and branch overload vulnerability index.

[0040] Furthermore, the low-voltage vulnerability index is evaluated as follows:

[0041]

[0042] Where P(Ei) represents the probability of a certain accident occurring;

[0043] S(V / Ei) represents the severity of the consequences following the accident;

[0044] UVR is a calculated low-voltage vulnerability index.

[0045] Furthermore, when the bus voltage is 1.0 pu, the severity is 0; when the bus voltage is 0.95 pu, the severity is 1, and the severity is linearly related to the bus voltage. When the bus voltage exceeds 1, the severity is 0.

[0046] Furthermore, the vulnerability index for branch circuit overload is evaluated as follows:

[0047]

[0048] Where P(Ei) represents the probability of a certain accident occurring;

[0049] The severity of the consequences of S(F / Ei);

[0050] OLR is a calculated low-voltage vulnerability index.

[0051] Furthermore, different active power flows correspond to different severity levels. When the actual power flow accounts for 90% of the rated value, the severity is 0; when the actual power flow accounts for 100% of the rated value, the severity is 1.0, and the severity is linearly related to the branch power flow. When the actual power flow is less than 90% of the rated value, the severity is 0.

[0052] The advantages and positive effects of this invention are:

[0053] This invention first establishes models for the output of several distributed power sources, such as wind turbines and photovoltaics, and analyzes the randomness of distributed power output. Second, it proposes two vulnerability indices for assessing the state of distribution networks: low voltage and overload. Based on these indices, it studies the calculation method for vulnerability indices of distribution networks containing multiple distributed power sources, further improving the practicality of the invention. Finally, it proposes a vulnerability assessment process for multi-energy distribution networks.

[0054] The vulnerability assessment method for distribution networks that considers multiple distributed energy sources proposed in this invention conducts in-depth research on the randomness of output and control strategies of distributed power sources in distribution networks, and derives an overall process for vulnerability assessment of multi-energy distribution networks. This reduces the predictability and unpredictability risks of distribution networks and provides a prerequisite for the safe and stable operation of distribution networks. Attached Figure Description

[0055] Figure 1 This is a function of the severity of low voltage in the method of this invention;

[0056] Figure 2 This is a function representing the severity of branch overload in the method of this invention;

[0057] Figure 3 This is a flowchart of the vulnerability assessment calculation for this invention. Detailed Implementation

[0058] The present invention will be further described in detail below through specific embodiments. The following embodiments are merely descriptive and not limiting, and should not be used to limit the scope of protection of the present invention.

[0059] This invention provides a vulnerability assessment method for multi-energy distribution networks, which is mainly implemented in four parts:

[0060] 1. Modeling the output of distributed power sources:

[0061] 1) Photovoltaics

[0062] G=Gb×SIF+(1-cc)×Gd+cc×tau×(Gb+Gd)

[0063] Where Gb represents the beam illumination condition;

[0064] Gd represents diffuse irradiance;

[0065] SIF simulates the unstable behavior of clouds in response to irradiance, with values ​​of 0 or 1.

[0066]

[0067] Where Ts is the sampling time;

[0068] LPtau is the time constant of the low-pass filter;

[0069] LPyOld is a placeholder for a low-pass filter.

[0070] After calculating the solar radiation, the photovoltaic output can be calculated.

[0071] pAvb = kPV × PF × Go

[0072] Where kPV is the conversion coefficient of light into electrical energy;

[0073] PF stands for power factor;

[0074] Go represents the solar radiation value.

[0075] 2) Fan

[0076]

[0077] Where V is the wind speed;

[0078] C is the scale parameter of the distribution, which reflects the average wind speed;

[0079] K is the shape parameter of the distribution.

[0080] PW = KWT × PF × V 3

[0081] Wherein, KWT is the conversion coefficient of wind speed into electrical energy;

[0082] PF stands for power factor.

[0083] The output of the gas turbine and capacitor bank can be adjusted according to actual needs, and its output value is relatively fixed during grid connection.

[0084] 2. Vulnerability assessment indicators and design principles

[0085] 1) Vulnerability index design principles:

[0086]

[0087] Where Yt represents a certain operating state of the system (such as voltage, power flow, etc.);

[0088] Ei represents an accident that may occur at some point in the near future;

[0089] L represents the load at each busbar at time t;

[0090] P(Yt / E,L) is the probability distribution of the power grid operating state after Ei appears;

[0091] S(Yt) represents the severity of the impact on the system after the accident.

[0092] R(Yt / E,L) is the calculated vulnerability index value.

[0093] The vulnerability index calculated using this method takes into account both the probability of an incident occurring and the severity of the consequences such an incident would have on the system, thus providing a relatively objective reflection of the network's vulnerability.

[0094] 2) Definition of Vulnerability Indicators

[0095] ①Low voltage vulnerability index

[0096]

[0097] Where P(Ei) represents the probability of a certain accident occurring;

[0098] S(V / Ei) represents the severity of the consequences following the accident;

[0099] UVR is a calculated low-voltage vulnerability index.

[0100] Depend on Figure 1 It can be seen that different voltage values ​​correspond to different severity levels. When the bus voltage is 1.0 pu, the severity is 0; when the bus voltage is 0.95 pu, the severity is 1. Furthermore, the severity is linearly related to the bus voltage. For simplicity, the severity is also set to 0 when the bus voltage exceeds 1 pu.

[0101] ② Branch overload vulnerability index

[0102] The power flow of each branch in the system is also an important indicator reflecting the system's operating status. The branch overload vulnerability index indicates the severity of active power overload in each branch and its consequences. The calculation formula is as follows:

[0103]

[0104] Where P(Ei) represents the probability of a certain accident occurring;

[0105] The severity of the consequences of S(F / Ei);

[0106] OLR is a calculated low-voltage vulnerability index.

[0107] Depend on Figure 2 It can be seen that different active power flows correspond to different severity levels. When the actual power flow accounts for 90% of the rated value, the severity is 0; when the actual power flow accounts for 100% of the rated value, the severity is 1.0. Furthermore, the severity has a linear relationship with the branch power flow. For simplicity of calculation, when the actual power flow is less than 90% of the rated value, the severity is also taken as 0.

[0108] 3. Distributed power supply control strategy

[0109] Since the addition of distributed generation to the distribution network will affect the system's operating status, such as voltage and power flow, and this effect can threaten the safe and stable operation of the system under some adverse conditions, it is essential to adopt certain control measures to control the output of distributed generation in real time.

[0110] Coordinated control measures are adopted for the output of distributed power sources. The operating status of each bus is transmitted to the central controller through the communication link. After calculation, the output value that needs to be adjusted is fed back to the distributed power source through the communication link to achieve coordinated control of multiple energy sources.

[0111] 4. Vulnerability Assessment Methods for Multi-Energy Distribution Networks

[0112] The vulnerability assessment method for distribution networks containing multiple distributed power sources includes: setting up original data, sampling wind speed using a distributed power source output model, calculating the output of the distributed power sources and performing power flow calculations to obtain the output control parameters of the distributed power sources, and simultaneously implementing coordinated control measures for the output of the distributed power sources. The operating status of each bus is transmitted to the central controller through a communication link. After calculation, the output value that needs to be adjusted is fed back to the distributed power sources through a communication link to achieve coordinated control of multiple energy sources. Then, it is determined whether the sampling is complete. If it is complete, the vulnerability index is calculated and the evaluation ends. If it is not complete, the sampling is repeated to obtain the wind speed.

[0113] Although embodiments of the invention have been disclosed for illustrative purposes, those skilled in the art will understand that various substitutions, variations, and modifications are possible without departing from the spirit and scope of the invention and the appended claims. Therefore, the scope of the invention is not limited to the contents disclosed in the embodiments.

Claims

1. A method for vulnerability assessment of a multi-energy power distribution network, characterized in that: The specific method comprises the following steps: setting original data, sampling wind speed by using a distributed power output model, calculating distributed power output and performing power flow calculation to obtain distributed power output control parameters, simultaneously taking coordinated control measures on the distributed power output, transmitting the operating state of each bus through a communication link to a central controller, calculating again, and then feeding back the output value needing to be adjusted to the distributed power through the communication link to realize coordinated control of multiple energies, and then judging whether the sampling is completed, calculating the vulnerability index and ending the evaluation if the sampling is completed, and returning to sample wind speed again if the sampling is not completed. 1) photovoltaic G=G b ×SIF+(1-cc)×G d +cc×tau×(G b +G d ) G b is the beam irradiation condition; G d represents the diffuse irradiance; SIF simulates the instability of cloud on irradiance, and takes 0 or 1, Ts is the sampling time; LPtau is the time constant of the low-pass filter; LPyOld is the placeholder of the low-pass filter, After the solar radiation condition is calculated, the photovoltaic output size can be calculated, p Avb =k PV ×PF×Go k PV is the conversion coefficient of light conversion into electric energy; PF is the power factor; Go is the solar radiation value; 2) wind turbine V is the wind speed; C is the scale parameter of distribution, reflecting the average wind speed; K is the shape parameter of distribution, PW = KWT x PF x V 3 KWT is the conversion coefficient of wind speed conversion into electric energy; PF is the power factor, The output of the gas turbine and the capacitor bank can be adjusted according to actual requirements, and the output value is relatively fixed during grid connection, The specific method comprises the following steps: Yt is a certain operating state of the system, including voltage, power flow, Ei is an accident that may occur at a certain time; L is the load condition of each bus at t; P(Yt / E,L) is the probability distribution of the operating state of the power grid after Ei occurs; S(Yt) is the severity of the impact on the system after the accident occurs; R(Yt / E,L) is the calculated vulnerability index value.

2. The method of claim 1, wherein: The vulnerability index definition includes a low-voltage vulnerability index and a branch overload vulnerability index.

3. The vulnerability evaluation method of the multi-energy distribution network according to claim 2, wherein: The low-voltage vulnerability index is evaluated as follows: P(Ei) is the possibility of a certain accident; S(V / Ei) is the severity of the consequences after the accident occurs; UVR is the calculated low-voltage vulnerability index.

4. The method of claim 3, wherein: When the bus voltage is 1.0pu, the severity is 0; when the bus voltage is 0.95pu, the severity is 1, and the severity and the bus voltage are in a linear relationship, and when the bus voltage exceeds 1, the severity is 0.

5. The vulnerability evaluation method of the multi-energy distribution network according to claim 2, wherein: The branch overload vulnerability index is evaluated as follows: P(Ei) is the possibility of a certain accident; S(F / Ei) is the severity of the consequences. OLR is the calculated low voltage vulnerability index.

6. The method of claim 5, wherein: Different pairs of active power flow correspond to different severity, when the actual flow is 90% of the rated value, the severity is 0; when the actual flow is 100% of the rated value, the severity is 1.0, and the severity is linearly related to the branch flow, when the actual flow is less than 90% of the rated value, the severity is 0.

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