Method and device for analyzing influence of distributed new energy output on power quality of power distribution network

Abstract

The invention discloses a method and a device for analyzing the influence of distributed new energy output on power distribution network electric energy quality. The method comprises the following steps: acquiring a correlation model between the output power of distributed new energy power generation equipment and a power distribution network electric energy quality index; performing simplification processing on the correlation model based on the short-line characteristics of the low-voltage power distribution network, and representing the electric energy quality index as a function relationship between the active power of the distributed new energy power generation equipment, the reactive power of the distributed new energy power generation equipment and the engineering coefficient; fitting the engineering coefficient through actual measurement data of the power distribution network to obtain an engineering simplified correlation model; and determining the influence of the distributed new energy output on the power quality of the power distribution network based on the engineering simplified correlation model. According to the method, the technical problems of large calculation data demand, high algorithm complexity and poor engineering practicability of the existing analysis method for the influence of the distributed new energy output on the power quality of the power distribution network are effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of power systems and new energy technologies, and in particular to a method and device for analyzing the impact of distributed new energy output on the power quality of a distribution network. Background Art

[0002] Currently, the integration of distributed renewable energy generation equipment into low-voltage distribution networks is increasing year by year, significantly changing the operational characteristics of these networks. Traditional low-voltage distribution networks with unidirectional power flows have gradually evolved into new active distribution networks characterized by complex and unpredictable active power fluctuations and bidirectional power flows. Distribution networks incorporating distributed renewable energy devices not only share the same regulatory challenges as traditional distribution networks, but also face new challenges arising from the integration of these devices. Analyzing the impact of distributed renewable energy device output on distribution network power quality can help better understand how these changes are impacting distribution network operation, providing an effective reference for grid operation and regulation, and possessing significant practical significance. However, existing analysis methods, based on power flow calculations and driven by data, are complex to implement, highly dependent on distribution network data, and require a large amount of data, making them inconvenient for practical engineering applications. Therefore, developing an analysis method for the impact of distributed renewable energy device output on distribution network power quality that requires minimal computational data, reduces algorithm complexity, and is amenable to practical engineering applications is an urgent technical challenge. Summary of the Invention

[0003] In order to solve at least one technical problem in the above-mentioned background technology, the present invention proposes a method and device for analyzing the impact of distributed renewable energy output on the power quality of a distribution network.

[0004] To achieve the above objectives, according to one aspect of the present invention, a method for analyzing the impact of distributed renewable energy output on power quality of a distribution network is provided, the method comprising:

[0005] Obtaining a correlation model between the output power of the distributed renewable energy power generation equipment and power quality indicators of the distribution network, wherein the power quality indicators include at least one of voltage deviation, third current harmonic, fifth current harmonic, voltage distortion rate, and three-phase imbalance;

[0006] The correlation model is simplified based on the short-line characteristics of the low-voltage distribution network, and the power quality index is expressed as a function of the active power of the distributed renewable energy power generation equipment, the reactive power of the distributed renewable energy power generation equipment, and the engineering coefficient;

[0007] Fitting the engineering coefficients using measured data from the distribution network to obtain an engineering-simplified correlation model;

[0008] The impact of distributed renewable energy output on the power quality of the distribution network is determined based on the engineering simplified correlation model.

[0009] Optionally, determining the impact of distributed renewable energy output on the power quality of the distribution network based on the engineering simplified correlation model includes:

[0010] The reactive power of the distributed renewable energy power generation equipment in the simplified engineering correlation model is set to 0, and then the predicted active power value of the distributed renewable energy power generation equipment is input into the simplified engineering correlation model to calculate the first indicator value of each power quality indicator;

[0011] Based on the first indicator value, the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network is determined.

[0012] Optionally, determining the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network based on the first indicator value includes:

[0013] If at least one of the first indicator values exceeds the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network exceeds the safe operation range;

[0014] If each of the first indicator values does not exceed the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network is within a safe operating range.

[0015] Optionally, the method further includes:

[0016] Determine the active power threshold of the distributed new energy power generation equipment corresponding to each of the power quality indicators according to the indicator threshold corresponding to each of the power quality indicators and the engineering simplified association model;

[0017] The minimum value among the active power thresholds of the power quality indicators is determined as the acceptance threshold of the active output of the distributed new energy equipment.

[0018] Optionally, determining the impact of distributed renewable energy output on the power quality of the distribution network based on the engineering simplified correlation model includes:

[0019] The active power of the distributed renewable energy power generation equipment in the simplified engineering association model is set to a preset value, and then the predicted reactive power value of the distributed renewable energy power generation equipment is input into the simplified engineering association model to calculate the second indicator value of each power quality indicator;

[0020] Based on the second indicator value, the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network is determined.

[0021] Optionally, determining the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network based on the second indicator value includes:

[0022] If at least one of the second indicator values exceeds the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network exceeds the safe operation range;

[0023] If each of the second indicator values does not exceed the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network is within a safe operating range.

[0024] Optionally, the method further includes:

[0025] Determine, based on the indicator threshold corresponding to each of the power quality indicators and the engineering simplified association model, a reactive power threshold of the distributed new energy power generation equipment corresponding to each of the power quality indicators;

[0026] The minimum value of the reactive power thresholds of the power quality indicators is determined as the acceptance threshold of the reactive output of the distributed new energy equipment.

[0027] To achieve the above objectives, according to another aspect of the present invention, a device for analyzing the impact of distributed renewable energy output on power quality of a distribution network is provided, the device comprising:

[0028] A correlation model acquisition unit is used to obtain a correlation model between the output power of the distributed renewable energy power generation equipment and the power quality index of the distribution network, wherein the power quality index includes at least one of voltage deviation, third current harmonic, fifth current harmonic, voltage distortion rate and three-phase imbalance;

[0029] A correlation model simplification processing unit is used to simplify the correlation model based on the short-line characteristics of the low-voltage distribution network, and express the power quality index as a function of the active power of the distributed new energy power generation equipment, the reactive power of the distributed new energy power generation equipment and the engineering coefficient;

[0030] An engineering simplified correlation model determination unit is used to fit the engineering coefficients using measured data of the distribution network to obtain the engineering simplified correlation model;

[0031] An impact analysis unit is used to determine the impact of distributed renewable energy output on the power quality of the distribution network based on the engineering simplified association model.

[0032] Optionally, the impact analysis unit includes:

[0033] a first indicator value calculation module, configured to set the reactive power of the distributed renewable energy power generation equipment in the simplified engineering association model to 0, and then input the predicted active power value of the distributed renewable energy power generation equipment into the simplified engineering association model to calculate a first indicator value for each of the power quality indicators;

[0034] The first impact determination module is used to determine the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network based on the first indicator value.

[0035] Optionally, the first impact determination module includes:

[0036] The first safe operating range judgment unit is used to determine that the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network exceeds the safe operating range if at least one of the first indicator values exceeds the indicator threshold of the corresponding power quality indicator; if each of the first indicator values does not exceed the indicator threshold of the corresponding power quality indicator, then it is determined that the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network is within the safe operating range.

[0037] Optionally, the device further includes:

[0038] an active power threshold determination unit, configured to determine the active power threshold of the distributed new energy power generation equipment corresponding to each of the power quality indicators based on the indicator threshold corresponding to each of the power quality indicators and the engineering simplified association model;

[0039] The active power output acceptance threshold determination unit is used to determine the minimum value of the active power thresholds of each of the power quality indicators as the acceptance threshold of the active power output of the distributed new energy equipment.

[0040] Optionally, the impact analysis unit includes:

[0041] a second indicator value calculation module, configured to set the active power of the distributed renewable energy power generation equipment in the simplified engineering association model to a preset value, and then input the predicted reactive power value of the distributed renewable energy power generation equipment into the simplified engineering association model to calculate a second indicator value for each of the power quality indicators;

[0042] The second impact determination module is used to determine the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network based on the second indicator value.

[0043] Optionally, the second impact determination module includes:

[0044] The second safe operating range judgment unit is used to determine that the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network exceeds the safe operating range if at least one of the second indicator values exceeds the indicator threshold of the corresponding power quality indicator; if each of the second indicator values does not exceed the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network is within the safe operating range.

[0045] Optionally, the device further includes:

[0046] a reactive power threshold determination unit, configured to determine a reactive power threshold of a distributed new energy power generation device corresponding to each of the power quality indicators based on the indicator threshold corresponding to each of the power quality indicators and the engineering simplified association model;

[0047] The reactive output acceptance threshold determination unit is used to determine the minimum value of the reactive power thresholds of each of the power quality indicators as the acceptance threshold of the reactive output of the distributed new energy equipment.

[0048] In order to achieve the above-mentioned purpose, according to another aspect of the present invention, a computer device is further provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for analyzing the impact of distributed renewable energy output on the power quality of the distribution network are implemented.

[0049] In order to achieve the above-mentioned purpose, according to another aspect of the present invention, a computer-readable storage medium is further provided, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the steps of the above-mentioned method for analyzing the impact of distributed renewable energy output on the power quality of the distribution network are implemented.

[0050] In order to achieve the above-mentioned purpose, according to another aspect of the present invention, a computer program product is also provided, including a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned method for analyzing the impact of distributed renewable energy output on the power quality of the distribution network.

[0051] The beneficial effects of the present invention are:

[0052] The embodiment of the present invention simplifies the correlation model between the output power of distributed renewable energy power generation equipment and the power quality index of the distribution network based on the short-line characteristics of the low-voltage distribution network, and expresses the power quality index as a function of the active power of the distributed renewable energy power generation equipment, the reactive power of the distributed renewable energy power generation equipment and the engineering coefficient. Then, the engineering coefficient is fitted through the measured data of the distribution network to obtain the engineering simplified correlation model. Finally, based on the engineering simplified correlation model, the impact of the distributed renewable energy output on the power quality of the distribution network is determined. This effectively solves the problems of large computational data requirements, high algorithm complexity and poor engineering practicality in the existing analysis method of the impact of the distributed renewable energy output on the power quality of the distribution network, and achieves beneficial effects such as improved computational efficiency, reduced data requirements and enhanced engineering applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0054] Figure 1 This is a flow chart of a method for analyzing the impact of distributed renewable energy output on power quality of a distribution network according to an embodiment of the present invention;

[0055] Figure 2 This is a flow chart of an embodiment of the present invention for determining the impact of the active power of distributed renewable energy power generation equipment on the power quality of the distribution network;

[0056] Figure 3 is a flow chart of determining an acceptance threshold of active output of distributed new energy equipment according to an embodiment of the present invention;

[0057] Figure 4 This is a flow chart of an embodiment of the present invention for determining the impact of reactive power of distributed renewable energy power generation equipment on the power quality of a distribution network;

[0058] Figure 5 is a flow chart of determining an acceptance threshold of reactive output of distributed new energy equipment according to an embodiment of the present invention;

[0059] Figure 6 This is a simplified schematic diagram of a distribution network for facilitating model construction according to an embodiment of the present invention;

[0060] Figure 7 This is a structural block diagram of a device for analyzing the impact of distributed renewable energy output on power quality of a distribution network according to an embodiment of the present invention;

[0061] Figure 8Schematic diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0063] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or apparatuses.

[0065] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0066] It should be noted that the device in the embodiment of the present invention specifically refers to distributed new energy power generation equipment.

[0067] The present invention proposes a method for analyzing the impact of distributed renewable energy output on the power quality of the distribution network. The method can be used to intuitively and efficiently quantify the impact of distributed renewable energy output on the power quality of the distribution network. Compared with traditional methods, it requires less data and has a low algorithm complexity, making it easier to use in actual projects.

[0068] Figure 1 FIG. 1 is a flow chart of a method for analyzing the impact of distributed renewable energy output on power quality of a distribution network according to an embodiment of the present invention. Figure 1 As shown, in one embodiment of the present invention, the method for analyzing the impact of distributed renewable energy output on power quality of distribution network includes steps S101 to S104.

[0069] Step S101: Obtain a correlation model between the output power of the distributed renewable energy power generation equipment and the power quality indicators of the distribution network, where the power quality indicators include at least one of voltage deviation, third current harmonic, fifth current harmonic, voltage distortion rate, and three-phase imbalance.

[0070] Step S102 , simplifying the correlation model based on the short-line characteristics of the low-voltage distribution network, and expressing the power quality index as a function of the active power of the distributed new energy power generation equipment, the reactive power of the distributed new energy power generation equipment and the engineering coefficient.

[0071] Step S103 , fitting the engineering coefficients using measured data of the distribution network to obtain the engineering simplified correlation model.

[0072] Step S104: determining the impact of the distributed renewable energy output on the power quality of the distribution network based on the engineering simplified correlation model.

[0073] In one embodiment of the present invention, the present invention first establishes a correlation model between the output power of distributed renewable energy power generation equipment and the power quality index of the distribution network (hereinafter referred to as the correlation model). The specific process of establishing the correlation model is as follows:

[0074] The present invention first establishes a simplified distribution network model suitable for model derivation, such as Figure 6 As shown, the transformer node in the distribution network area is considered to be an ideal voltage source, and its voltage is expressed as U N , represents the standard value of the distribution network voltage. f is the equivalent resistance from the distributed renewable energy power generation equipment insertion node to the transformer node, X f is the equivalent reactance from the distributed renewable energy generation equipment insertion node to the transformer node, I f It is the equivalent current from the intervention node of distributed renewable energy power generation equipment to the transformer node.

[0075] Define the node voltage before the device (distributed new energy power generation equipment) sends power as U before , after sending out power, it is U after , according to the circuit theorem, the equation can be listed:

[0076] U after =U before +I f (R f +jX f ) (1)

[0077] Among them, I f It can be expressed as:

[0078]

[0079] In formula (2), P out is the active power (i.e. active output) of distributed new energy power generation equipment, Q out is the reactive power (i.e. reactive output) of the distributed renewable energy power generation equipment, and g is the correction factor added to consider the load distribution of the distribution network and the power output of other renewable energy equipment.

[0080] Then the expression of node voltage is obtained as:

[0081]

[0082] According to the definition of voltage deviation:

[0083]

[0084] The formula of the correlation model between the output power of distributed renewable energy power generation equipment and the voltage deviation of the distribution network is derived as follows:

[0085]

[0086] Define two variables α and β. Different values of these variables represent different control strategies for the inverter device. Their specific values are shown in Table 1:

[0087] Table 1 Relationship between control strategy and variable values

[0088] Control strategy α β Instantaneous power factor control 1 1 Average power factor control 1 0 Instantaneous positive sequence control 0 1 Average positive sequence control 0 0 Positive and negative sequence compensation control -1 1

[0089] Finally, the formula of the output power of distributed new energy power generation equipment and the current harmonics of the distribution network is obtained:

[0090]

[0091] In formula (6), A=β(1+αn), B=1+αn 2 , I 3th , I 5th Respectively represent the third and fifth harmonic current amplitudes after the output power of the equipment, I 3thb , I 5thb They represent the amplitudes of the third and fifth harmonic currents before the equipment output power, respectively, and n is the imbalance of the grid voltage before the grid-connected equipment outputs power.

[0092] The formula of the correlation model between the output power of distributed renewable energy power generation equipment and the voltage distortion rate of the distribution network is derived as follows:

[0093]

[0094] In formula (7), THD uRefers to the voltage distortion rate.

[0095] Considering the most likely imbalance in the actual distribution network, it is assumed that the imbalance is caused by single-phase power access to the grid. Here, it is assumed that phase A is connected to the converter to transmit power to the grid, thereby causing a single-phase voltage deviation, while the other two phases remain unchanged. The formula for establishing the correlation model between the output power of distributed renewable energy power generation equipment and the three-phase imbalance of the distribution network is:

[0096]

[0097] In formula (8), U a_before U is the voltage on one phase of the output power of the grid-connected device before the grid-connected device outputs power at the access node. b_before 、U c_before It is the voltage on the other two phases of the access node before the grid-connected device outputs power, except for the one phase where the grid-connected device outputs power.

[0098] The correlation model between the output power of the distributed renewable energy power generation equipment and the power quality index of the distribution network obtained in the above step S101 of the present invention is specifically the correlation model of the voltage deviation of the above formula (5), the correlation model of the third current harmonic and the fifth current harmonic of the above formula (6), the correlation model of the voltage distortion rate of the above formula (7) and the correlation model of the three-phase imbalance of the above formula (8).

[0099] In step S102, the present invention simplifies the correlation model based on the short-circuit characteristics of the low-voltage distribution network, and expresses the power quality index as a functional relationship between the active power of the distributed renewable energy power generation equipment, the reactive power of the distributed renewable energy power generation equipment, and the engineering coefficient. The specific simplified processing process is as follows:

[0100] First, considering that the line distance of the low-voltage distribution network is short, the phase angle difference of the node voltage is not large, so the above node voltage expression (3) is simplified to obtain the simplified node voltage expression:

[0101]

[0102] Based on the simplified node voltage expression (9), the correlation model of the above power quality indicators is simplified, and each power quality indicator is expressed as the active power P of the distributed new energy power generation equipment. out , the reactive power Q of distributed new energy power generation equipment out Functional relationship with engineering coefficient (K series coefficient).

[0103] Specifically, the simplified correlation model of voltage deviation is the following formula (10):

[0104]

[0105] In formula (10), ΔU is the voltage deviation, K Δ , K ΔP and k are three engineering coefficients, which can be subsequently calculated by data fitting.

[0106] The simplified correlation model of voltage distortion rate is the following formula (11):

[0107]

[0108] In formula (11), THD u is the voltage deviation, and are three engineering coefficients, which can be subsequently calculated by data fitting. The k in formula (11) is the same as that in formula (10) and can be calculated by data fitting formula (10).

[0109] The simplified correlation model of three-phase imbalance is the following formula (12):

[0110]

[0111] In formula (12), ε is the three-phase imbalance, K ε and K εP These are two engineering coefficients, which can be subsequently calculated through data fitting.

[0112] The simplified correlation model of the third current harmonic is the following formula (13):

[0113]

[0114] In formula (13), I 3th is the third current harmonic, K 3th , K 3th1 and K 3th2 These are three engineering coefficients, which can be calculated by data fitting. K3 is an intermediate coefficient. Represents the apparent power output by the inverter. The k in formula (13) is the same as that in formula (10) and can be calculated by fitting the data of formula (10).

[0115] The simplified correlation model of the fifth current harmonic is the following formula (14):

[0116]

[0117] In formula (14), I 5th is the fifth current harmonic, K5th , K 5th1 and K 5th2 These are three engineering coefficients, which can be calculated by data fitting. K5 is an intermediate coefficient. Represents the apparent power output by the inverter. The k in formula (14) is the same as that in formula (10) and can be calculated by fitting the data of formula (10).

[0118] In step S103, the present invention fits and solves the engineering coefficients (K series coefficients) in the above-mentioned simplified correlation models (Formula (10), Formula (11), Formula (12), Formula (13) and Formula (14)) through the measured data of the distribution network to obtain an engineered simplified correlation model.

[0119] The engineering coefficients (K series coefficients) in the simplified correlation models can be regarded as constant values during the normal operation of the distribution network, so the engineering coefficients (K series coefficients) can be solved by obtaining the measured data of the distribution network through a sampling device.

[0120] Each of the simplified correlation models above contains a maximum of three engineering coefficients. The sampling device needs to collect at least three sets of measured data from the distribution network to calculate the three engineering coefficients. The three sets of measured data collected by the sampling device are as follows:

[0121]

[0122] After obtaining the measured data, the equations can be solved by the method of undetermined coefficients to obtain the engineering coefficients (K series coefficients) in each simplified correlation model, thereby establishing a simplified correlation model between the output power of distributed renewable energy power generation equipment and the power quality indicators of the distribution network that is suitable for engineering practice, that is, the engineering simplified correlation model in the above step S103.

[0123] The present invention simplifies the complex power quality assessment model into a model suitable for actual engineering through the introduction and calibration of engineering coefficients (K series coefficients). Each simplified correlation model (such as voltage deviation, harmonic current, voltage distortion rate, and three-phase imbalance model) contains at most three engineering coefficients, which can be regarded as constants during the normal operation of the distribution network. The measured data of the distribution network is obtained through a sampling device, and these engineering coefficients are solved using the method of undetermined coefficients, thereby establishing a simplified version of the correlation model between the output power of distributed renewable energy power generation equipment and the power quality indicators of the distribution network. This method of the present invention significantly reduces the computational complexity and data requirements of the model, while improving the engineering applicability and practicality of the model.

[0124] like Figure 2As shown, in one embodiment of the present invention, the above-mentioned step S104 of determining the impact of distributed renewable energy output on the power quality of the distribution network based on the engineering simplified correlation model includes step S201 and step S202.

[0125] Step S201: Set the reactive power of the distributed renewable energy power generation equipment in the simplified engineering association model to 0, then input the predicted active power value of the distributed renewable energy power generation equipment into the simplified engineering association model, and calculate the first indicator value of each of the power quality indicators.

[0126] Step S202: determining the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network based on the first indicator value.

[0127] The present invention analyzes the impact of distributed renewable energy output on distribution network power quality, including analyzing the impact of the active power of distributed renewable energy generation equipment on distribution network power quality. Based on an engineered, simplified correlation model, the present invention quantitatively analyzes the relationship between distributed renewable energy active output (active output power) and power quality.

[0128] Taking into account the operating characteristics of low-voltage distributed new energy equipment, it does not generate reactive power during normal operation, so the reactive power Q of distributed new energy power generation equipment is converted into out Set to 0.

[0129] For distributed new energy equipment, if its active output (active output power) within a certain period of time is predicted to be P p1 Substituting this into the simplified engineering correlation model described above, we can calculate the values for each power quality indicator. Based on these calculated values, we can assess the specific impact of the device's active output on the distribution network's power quality and determine whether it causes a specific power quality indicator to exceed its limit. The smaller the calculated power quality indicator, the less impact the distributed renewable energy device has on the distribution network's power quality under the same active output conditions, and vice versa.

[0130] In one embodiment of the present invention, the above-mentioned step S202 of determining the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network based on the first indicator value includes:

[0131] If at least one of the first indicator values exceeds the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network exceeds the safe operation range;

[0132] If each of the first indicator values does not exceed the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network is within a safe operating range.

[0133] In one embodiment of the present invention, the indicator thresholds of various power quality indicators may be as shown in Table 2 below.

[0134] Table 2 Main power quality indicators and their thresholds

[0135] symbol definition Threshold ΔU Voltage deviation ≤7％ <![CDATA[I 3h ]]> Third current harmonic ≤62A@10MVA <![CDATA[I 5h ]]> Fifth current harmonic ≤62A@10MVA <![CDATA[THD U ]]> Voltage distortion rate ≤5％ ε Three-phase imbalance ≤2％

[0136] The sources of indicator thresholds are: GB / T12325, GB / T14549, and GB / T15543.

[0137] like Figure 3 As shown, in one embodiment of the present invention, the method for analyzing the impact of distributed renewable energy output on power quality of distribution network of the present invention further includes step S301 and step S302.

[0138] Step S301: Determine the active power threshold of the distributed new energy power generation equipment corresponding to each power quality indicator according to the indicator threshold corresponding to each power quality indicator and the engineering simplified association model.

[0139] Step S302: determining the minimum value among the active power thresholds of the power quality indicators as the acceptance threshold for the active output of the distributed new energy equipment.

[0140] In the present invention, in view of the different requirements of different industries for power quality indicators, the thresholds of tolerable power quality indicators can be specified according to the power consumption characteristics of the industry, thereby calculating the active output thresholds under the constraints of each indicator:

[0141]

[0142] In formula (16), ΔU lim 、THD ulim , ε lim , I 3thlim , I 5thlim They are the thresholds of power quality indicators specified according to the power consumption characteristics of the industry, corresponding to the voltage deviation indicator, voltage distortion rate indicator, three-phase imbalance indicator, third current harmonic indicator, and fifth current harmonic indicator respectively. max1 is the active power threshold of the distributed new energy power generation equipment corresponding to the voltage deviation index, P max2 is the active power threshold of the distributed new energy power generation equipment corresponding to the voltage distortion rate index, P max3is the active power threshold of the distributed renewable energy power generation equipment corresponding to the three-phase imbalance index, P max4 is the active power threshold of the distributed new energy power generation equipment corresponding to the third current harmonic index, P max5 It is the active power threshold of distributed new energy power generation equipment corresponding to the fifth current harmonic index.

[0143] The present invention compares the calculated active output thresholds and selects the minimum value as the acceptance threshold of the active output of the distributed new energy equipment, as shown in the following formula (17). The acceptance threshold of the active output of the distributed new energy equipment refers to the maximum value of the acceptable distributed new energy active output power.

[0144] P max ={P max1 ,P max2 ,P max3 ,P max4 ,P max5} min (17)

[0145] P max It represents the acceptance threshold of the active power output of distributed new energy equipment. The larger the value, the more active power the equipment can access, and vice versa.

[0146] like Figure 4 As shown, in one embodiment of the present invention, the above-mentioned step S104 of determining the impact of distributed renewable energy output on the power quality of the distribution network based on the engineering simplified correlation model includes step S401 and step S402.

[0147] Step S401: Set the active power of the distributed renewable energy power generation equipment in the simplified engineering association model to a preset value, then input the predicted reactive power value of the distributed renewable energy power generation equipment into the simplified engineering association model, and calculate the second indicator value of each of the power quality indicators.

[0148] Step S402: determining the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network based on the second indicator value.

[0149] The present invention analyzes the impact of distributed renewable energy output on the power quality of the distribution network, further comprising analyzing the impact of the reactive power of distributed renewable energy generation equipment on the power quality of the distribution network. Based on the simplified engineering model described above, the present invention quantitatively analyzes the relationship between the reactive output (reactive output power) of distributed renewable energy and power quality.

[0150] The purpose of evaluating the relationship between reactive output (reactive output power) and power quality is to use the remaining capacity of the equipment for reactive compensation and thus improve a certain power quality indicator of the low-voltage distribution network. In the actual operation of the equipment, it is necessary to ensure that its output active power is maximized to ensure the economic benefits of the connected equipment. Therefore, when evaluating the reactive access capacity, the active power P out It is considered as a fixed value for calculation.

[0151] For distributed new energy equipment, if its reactive output (reactive output power) within a certain period of time is predicted to be P p1 Substituting this into the simplified engineering correlation model described above, we can calculate the values for each power quality indicator. Based on these calculated values, we can assess the specific impact of the device's reactive output on the distribution network's power quality and determine whether it causes a specific power quality indicator to exceed its limit. The smaller the calculated power quality indicator, the less impact the distributed renewable energy device has on the distribution network's power quality under the same reactive output conditions, and vice versa.

[0152] In one embodiment of the present invention, the above-mentioned step S402 of determining the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network based on the second indicator value includes:

[0153] If at least one of the second indicator values exceeds the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network exceeds the safe operation range;

[0154] If each of the second indicator values does not exceed the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network is within a safe operating range.

[0155] like Figure 5 As shown, in one embodiment of the present invention, the method for analyzing the impact of distributed renewable energy output on power quality of distribution network of the present invention further includes step S501 and step S502.

[0156] Step S501: Determine the reactive power threshold of the distributed new energy power generation equipment corresponding to each power quality indicator according to the indicator threshold corresponding to each power quality indicator and the engineering simplified association model.

[0157] Step S502: determining the minimum value among the reactive power thresholds of the power quality indicators as the acceptance threshold of the reactive power output of the distributed new energy equipment.

[0158] In the present invention, in view of the different requirements of different industries for power quality indicators, the thresholds of tolerable power quality indicators can be specified according to the power consumption characteristics of each industry, thereby calculating the reactive output thresholds under the constraints of each power quality indicator:

[0159]

[0160] In formula (18), ΔU lim 、THD ulim , ε lim , I 3thlim , I 5thlim These are the thresholds of power quality indicators specified according to the power consumption characteristics of the industry, corresponding to the voltage deviation indicator, voltage distortion rate indicator, three-phase imbalance indicator, third current harmonic indicator, and fifth current harmonic indicator. max1 is the reactive power threshold of distributed renewable energy power generation equipment corresponding to the voltage deviation index, Q max2 is the reactive power threshold of distributed renewable energy power generation equipment corresponding to the voltage distortion rate index, Q max3 is the reactive power threshold of the distributed renewable energy power generation equipment corresponding to the three-phase imbalance index, Q max4 is the reactive power threshold of distributed new energy power generation equipment corresponding to the third current harmonic index, Q max5 It is the reactive power threshold of distributed new energy power generation equipment corresponding to the fifth current harmonic index.

[0161] The present invention compares the calculated reactive power thresholds and selects the minimum value as the acceptance threshold of the reactive output of the distributed new energy equipment, as shown in the following formula (19). The acceptance threshold of the reactive output of the distributed new energy equipment refers to the maximum value of the acceptable distributed new energy reactive output power.

[0162] Q max ={Q max1 ,Q max2 ,Q max3 ,Q max4 ,Q max5} min (19)

[0163] Q max It represents the acceptable threshold of reactive power output of distributed new energy equipment. The larger the value, the more active power the equipment can access, and vice versa.

[0164] It can be seen from the above embodiments that the method of the present invention achieves at least the following beneficial effects:

[0165] 1. Significantly reduce the amount of computing data required.

[0166] This invention simplifies complex power quality assessment models into simplified models suitable for practical applications by introducing engineering coefficients (K-series coefficients) and calibrating them using a small amount of measured data. While traditional methods typically require extensive real-time monitoring data and high-precision parameters (such as line impedance and load distribution), this approach only requires a small amount of measured data (e.g., three sets of data) to complete coefficient calibration, significantly reducing data requirements.

[0167] 2. Greatly reduce the complexity of the algorithm.

[0168] Traditional power quality assessment methods typically rely on detailed circuit models for simulation calculations, which are computationally intensive and difficult to meet real-time requirements. This invention simplifies complex nonlinear models into linear expressions through linearization and parameter aggregation, significantly reducing algorithmic complexity. This invention simplifies the calculation process through linearization approximation and coefficient calibration, enabling the model to rapidly respond to dynamic changes in the distribution network. This invention not only improves computational efficiency but also supports real-time assessment and control, providing distribution network operators with fast and accurate power quality assessment results.

[0169] 3. Improve the applicability and practicality of the project.

[0170] The simplified model of the present invention is not only computationally efficient, but also easy to deploy and apply in actual projects. Through the dynamic calibration of engineering coefficients, the model can adapt to different distribution network operating conditions (such as line length, load distribution, equipment type, etc.), and has strong versatility and flexibility. In addition, the simplified model supports real-time evaluation and control, and can provide distribution network operators with fast and accurate power quality assessment results, thereby optimizing the output strategy of distributed new energy equipment and improving the safety and stability of the power grid. The method of the present invention significantly improves the engineering applicability and practicality of the model, and provides an efficient and practical solution for the power quality assessment of distributed new energy power generation equipment connected to the distribution network.

[0171] 4. Realize comprehensive quantitative analysis of multiple indicators.

[0172] The present invention realizes a comprehensive quantitative analysis of multiple power quality indicators (such as voltage deviation, harmonic current, voltage distortion rate, and three-phase imbalance) by constructing a correlation model between the output power of distributed renewable energy power generation equipment and the power quality parameters of the distribution network. Traditional methods can usually only evaluate a single indicator, but the present invention can comprehensively evaluate the impact of distributed renewable energy equipment on the power quality of the distribution network through multi-indicator integrated modeling. For example, through the comprehensive application of voltage deviation model, harmonic current model, voltage distortion rate model and three-phase imbalance model, the specific impact of the active / reactive output of distributed renewable energy equipment on the power quality of the distribution network can be accurately quantified, thereby providing comprehensive decision support for distribution network operators.

[0173] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0174] Based on the same inventive concept, an embodiment of the present invention also provides an apparatus for analyzing the impact of distributed renewable energy output on the power quality of a distribution network, which can be used to implement the method for analyzing the impact of distributed renewable energy output on the power quality of a distribution network described in the above embodiment, as described in the following embodiment. Since the principle of solving the problem by the apparatus for analyzing the impact of distributed renewable energy output on the power quality of a distribution network is similar to the method for analyzing the impact of distributed renewable energy output on the power quality of a distribution network, the embodiment of the apparatus for analyzing the impact of distributed renewable energy output on the power quality of a distribution network can refer to the embodiment of the method for analyzing the impact of distributed renewable energy output on the power quality of a distribution network, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceived.

[0175] Figure 7 This is a structural block diagram of a device for analyzing the impact of distributed renewable energy output on power quality of a distribution network according to an embodiment of the present invention. Figure 7 As shown, in one embodiment of the present invention, the device for analyzing the impact of distributed renewable energy output on power quality of distribution network of the present invention includes:

[0176] Correlation model acquisition unit 1, used to obtain a correlation model between the output power of the distributed renewable energy power generation equipment and the power quality index of the distribution network, wherein the power quality index includes at least one of voltage deviation, third current harmonic, fifth current harmonic, voltage distortion rate and three-phase imbalance;

[0177] The correlation model simplification processing unit 2 is used to simplify the correlation model based on the short-line characteristics of the low-voltage distribution network, and express the power quality index as a function of the active power of the distributed new energy power generation equipment, the reactive power of the distributed new energy power generation equipment and the engineering coefficient;

[0178] The engineering simplified correlation model determination unit 3 is used to fit the engineering coefficients using measured data of the distribution network to obtain the engineering simplified correlation model;

[0179] The impact analysis unit 4 is used to determine the impact of the distributed renewable energy output on the power quality of the distribution network based on the engineering simplified association model.

[0180] In one embodiment of the present invention, the impact analysis unit includes:

[0181] a first indicator value calculation module, configured to set the reactive power of the distributed renewable energy power generation equipment in the simplified engineering association model to 0, and then input the predicted active power value of the distributed renewable energy power generation equipment into the simplified engineering association model to calculate a first indicator value for each of the power quality indicators;

[0182] The first impact determination module is used to determine the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network based on the first indicator value.

[0183] In one embodiment of the present invention, the first impact determination module includes:

[0184] The first safe operating range judgment unit is used to determine that the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network exceeds the safe operating range if at least one of the first indicator values exceeds the indicator threshold of the corresponding power quality indicator; if each of the first indicator values does not exceed the indicator threshold of the corresponding power quality indicator, then it is determined that the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network is within the safe operating range.

[0185] In one embodiment of the present invention, the device for analyzing the impact of distributed renewable energy output on power quality of a distribution network of the present invention further includes:

[0186] an active power threshold determination unit, configured to determine the active power threshold of the distributed new energy power generation equipment corresponding to each of the power quality indicators based on the indicator threshold corresponding to each of the power quality indicators and the engineering simplified association model;

[0187] The active power output acceptance threshold determination unit is used to determine the minimum value of the active power thresholds of each of the power quality indicators as the acceptance threshold of the active power output of the distributed new energy equipment.

[0188] In one embodiment of the present invention, the impact analysis unit includes:

[0189] a second indicator value calculation module, configured to set the active power of the distributed renewable energy power generation equipment in the simplified engineering association model to a preset value, and then input the predicted reactive power value of the distributed renewable energy power generation equipment into the simplified engineering association model to calculate a second indicator value for each of the power quality indicators;

[0190] The second impact determination module is used to determine the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network based on the second indicator value.

[0191] In one embodiment of the present invention, the second impact determination module includes:

[0192] The second safe operating range judgment unit is used to determine that the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network exceeds the safe operating range if at least one of the second indicator values exceeds the indicator threshold of the corresponding power quality indicator; if each of the second indicator values does not exceed the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network is within the safe operating range.

[0193] In one embodiment of the present invention, the device for analyzing the impact of distributed renewable energy output on power quality of a distribution network of the present invention further includes:

[0194] a reactive power threshold determination unit, configured to determine a reactive power threshold of a distributed new energy power generation device corresponding to each of the power quality indicators based on the indicator threshold corresponding to each of the power quality indicators and the engineering simplified association model;

[0195] The reactive output acceptance threshold determination unit is used to determine the minimum value of the reactive power thresholds of each of the power quality indicators as the acceptance threshold of the reactive output of the distributed new energy equipment.

[0196] In order to achieve the above object, according to another aspect of the present application, a computer device is also provided. Figure 8 As shown, the computer device includes a memory, a processor, a communication interface and a communication bus. The memory stores a computer program that can be run on the processor. When the processor executes the computer program, the steps in the above embodiment method are implemented.

[0197] The processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0198] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and units, such as the corresponding program units in the above-described method embodiments of the present invention. The processor executes the non-transitory software programs, instructions, and modules stored in memory to perform various processor functions and work data processing, thereby implementing the methods in the above-described method embodiments.

[0199] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0200] The one or more units are stored in the memory, and when executed by the processor, perform the method in the above embodiment.

[0201] The specific details of the above-mentioned computer device can be understood by referring to the corresponding descriptions and effects in the above-mentioned embodiments, and will not be repeated here.

[0202] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a computer-readable storage medium is further provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed in a computer processor, the steps in the above-mentioned method for analyzing the impact of distributed renewable energy output on the power quality of the distribution network are implemented. Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment method can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0203] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a computer program product is also provided, including a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned method for analyzing the impact of distributed renewable energy output on the power quality of the distribution network.

[0204] Obviously, those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computing device. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0205] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for analyzing the impact of distributed renewable energy output on power quality of distribution network, characterized in that: include: Obtaining a correlation model between the output power of the distributed renewable energy power generation equipment and power quality indicators of the distribution network, wherein the power quality indicators include at least one of voltage deviation, third current harmonic, fifth current harmonic, voltage distortion rate, and three-phase imbalance; The correlation model is simplified based on the short-line characteristics of the low-voltage distribution network, and the power quality index is expressed as a function of the active power of the distributed renewable energy power generation equipment, the reactive power of the distributed renewable energy power generation equipment, and the engineering coefficient; Fitting the engineering coefficients using measured data from the distribution network to obtain an engineering-simplified correlation model; The impact of distributed renewable energy output on the power quality of the distribution network is determined based on the engineering simplified correlation model.

2. The method for analyzing the impact of distributed renewable energy output on power quality of distribution network according to claim 1 is characterized in that: The determining of the impact of distributed renewable energy output on the power quality of the distribution network based on the engineering simplified correlation model includes: The reactive power of the distributed renewable energy power generation equipment in the simplified engineering correlation model is set to 0, and then the predicted active power value of the distributed renewable energy power generation equipment is input into the simplified engineering correlation model to calculate the first indicator value of each power quality indicator; Based on the first indicator value, the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network is determined.

3. The method for analyzing the impact of distributed renewable energy output on power quality of distribution network according to claim 2 is characterized in that: The determining, based on the first indicator value, the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network includes: If at least one of the first indicator values exceeds the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network exceeds the safe operation range; If each of the first indicator values does not exceed the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network is within a safe operating range.

4. The method for analyzing the impact of distributed renewable energy output on power quality of distribution network according to claim 3 is characterized in that: Also includes: Determine the active power threshold of the distributed new energy power generation equipment corresponding to each of the power quality indicators according to the indicator threshold corresponding to each of the power quality indicators and the engineering simplified association model; The minimum value among the active power thresholds of the power quality indicators is determined as the acceptance threshold of the active output of the distributed new energy equipment.

5. The method for analyzing the impact of distributed renewable energy output on power quality of distribution network according to claim 1 is characterized in that: The determining of the impact of distributed renewable energy output on the power quality of the distribution network based on the engineering simplified correlation model includes: The active power of the distributed renewable energy power generation equipment in the simplified engineering association model is set to a preset value, and then the predicted reactive power value of the distributed renewable energy power generation equipment is input into the simplified engineering association model to calculate the second indicator value of each power quality indicator; Based on the second indicator value, the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network is determined.

6. The method for analyzing the impact of distributed renewable energy output on power quality of distribution network according to claim 5 is characterized in that: The determining, based on the second indicator value, the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network includes: If at least one of the second indicator values exceeds the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network exceeds the safe operation range; If each of the second indicator values does not exceed the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network is within a safe operating range.

7. The method for analyzing the impact of distributed renewable energy output on power quality of distribution network according to claim 6 is characterized in that: Also includes: Determine, based on the indicator threshold corresponding to each of the power quality indicators and the engineering simplified association model, a reactive power threshold of the distributed new energy power generation equipment corresponding to each of the power quality indicators; The minimum value of the reactive power thresholds of the power quality indicators is determined as the acceptance threshold of the reactive output of the distributed new energy equipment.

8. A device for analyzing the impact of distributed renewable energy output on power quality of distribution network, characterized in that: include: A correlation model acquisition unit is used to obtain a correlation model between the output power of the distributed renewable energy power generation equipment and the power quality index of the distribution network, wherein the power quality index includes at least one of voltage deviation, third current harmonic, fifth current harmonic, voltage distortion rate and three-phase imbalance; A correlation model simplification processing unit is used to simplify the correlation model based on the short-line characteristics of the low-voltage distribution network, and express the power quality index as a function of the active power of the distributed new energy power generation equipment, the reactive power of the distributed new energy power generation equipment and the engineering coefficient; An engineering simplified correlation model determination unit is used to fit the engineering coefficients using measured data of the distribution network to obtain the engineering simplified correlation model; An impact analysis unit is used to determine the impact of distributed renewable energy output on the power quality of the distribution network based on the engineering simplified association model.

9. The device for analyzing the impact of distributed renewable energy output on power quality of distribution network according to claim 8, characterized in that: The impact analysis unit includes: a first indicator value calculation module, configured to set the reactive power of the distributed renewable energy power generation equipment in the simplified engineering association model to 0, and then input the predicted active power value of the distributed renewable energy power generation equipment into the simplified engineering association model to calculate a first indicator value for each of the power quality indicators; The first impact determination module is used to determine the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network based on the first indicator value.

10. The device for analyzing the impact of distributed renewable energy output on power quality of distribution network according to claim 9, characterized in that: The first impact determination module includes: The first safe operating range judgment unit is used to determine that the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network exceeds the safe operating range if at least one of the first indicator values exceeds the indicator threshold of the corresponding power quality indicator; if each of the first indicator values does not exceed the indicator threshold of the corresponding power quality indicator, then it is determined that the impact of the active power of the distributed new energy power generation equipment on the power quality of the distribution network is within the safe operating range.

11. The device for analyzing the impact of distributed renewable energy output on power quality of distribution network according to claim 10, characterized in that: Also includes: an active power threshold determination unit, configured to determine the active power threshold of the distributed new energy power generation equipment corresponding to each of the power quality indicators based on the indicator threshold corresponding to each of the power quality indicators and the engineering simplified association model; The active power output acceptance threshold determination unit is used to determine the minimum value of the active power thresholds of each of the power quality indicators as the acceptance threshold of the active power output of the distributed new energy equipment.

12. The device for analyzing the impact of distributed renewable energy output on power quality of distribution network according to claim 8, characterized in that: The impact analysis unit includes: a second indicator value calculation module, configured to set the active power of the distributed renewable energy power generation equipment in the simplified engineering association model to a preset value, and then input the predicted reactive power value of the distributed renewable energy power generation equipment into the simplified engineering association model to calculate a second indicator value for each of the power quality indicators; The second impact determination module is used to determine the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network based on the second indicator value.

13. The device for analyzing the impact of distributed renewable energy output on power quality of distribution network according to claim 12, characterized in that: The second impact determination module includes: The second safe operating range judgment unit is used to determine that the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network exceeds the safe operating range if at least one of the second indicator values exceeds the indicator threshold of the corresponding power quality indicator; if each of the second indicator values does not exceed the indicator threshold of the corresponding power quality indicator, it is determined that the impact of the reactive power of the distributed new energy power generation equipment on the power quality of the distribution network is within the safe operating range.

14. The device for analyzing the impact of distributed renewable energy output on power quality of distribution network according to claim 13, characterized in that: Also includes: a reactive power threshold determination unit, configured to determine a reactive power threshold of a distributed new energy power generation device corresponding to each of the power quality indicators based on the indicator threshold corresponding to each of the power quality indicators and the engineering simplified association model; The reactive output acceptance threshold determination unit is used to determine the minimum value of the reactive power thresholds of each of the power quality indicators as the acceptance threshold of the reactive output of the distributed new energy equipment.

15. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

17. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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