Multi-energy collaborative complementary power grid access capability assessment method

Abstract

The invention discloses a multi-energy collaborative complementary power grid access capability assessment method, and relates to the technical field of electric power. The multi-energy collaborative complementary power grid access capability assessment method comprises the three steps of S1 data collection and preprocessing, S2 characteristic analysis module analysis and S3 access capability comprehensive assessment, and by constructing a renewable energy power generation facility characteristic library, collecting output sample information, load sample information and the like, the output characteristics and load characteristics of different renewable energy power supplies are analyzed, and the access capability of the power grid is assessed. The transmission capability, the operation characteristics and the like of the power grid are analyzed, development potential evaluation, access capability evaluation, future output and load prediction, equipment bearing upper limit determination and power grid weak link identification are finally carried out, and development potential evaluation comprises illumination resource endowment evaluation, geographic attribute constraint evaluation and economic cost constraint evaluation. The multi-energy collaborative complementary power grid access capability assessment method has the beneficial effects of multiple functions, accurate assessment and high practicability.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method for evaluating the access capability of a power grid with coordinated and complementary multi-energy sources. Background Art

[0002] With the rapid development of renewable energy technology, renewable energy forms such as solar photovoltaic and wind power generation have been widely adopted due to their environmental protection and sustainable characteristics. However, these renewable energy generation methods have significant intermittency and uncertainty, which brings unprecedented challenges to the stable operation of the power system.

[0003] In addition, renewable energy facilities are usually widely distributed, which increases the complexity of grid dispatch and management. Existing renewable energy access assessment methods mostly rely on static data analysis or empirical rules, which makes it difficult to accurately predict the maximum access capacity of renewable energy under dynamic conditions and its impact on grid stability.

[0004] Therefore, the prior art needs to be improved. Summary of the invention

[0005] The technical problem to be solved by the present invention is that the existing renewable energy access assessment method is relatively poor in effect. The purpose is to provide a multi-energy coordinated and complementary power grid access capacity assessment method, which adopts corresponding technical solutions and has the beneficial effects of diverse functions, accurate assessment and strong practicality.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for evaluating the access capability of a multi-energy grid with coordinated complementarity, comprising the following contents:

[0008] S1 Data collection and preprocessing: Build a renewable energy power generation facility characteristic database, which includes output sample information, load sample information, and cross-section operation sample information of renewable energy type power generation facilities in the target area that summarizes the grid operation status. It also includes weather data, market electricity price information, and power policy and regulatory factors in the target area.

[0009] S2 characteristic analysis module analysis: Based on the characteristic library established in S1, analyze the output characteristics of different renewable energy sources; analyze the transmission capacity and operation characteristics of the power grid; analyze the load characteristics of different types of loads; analyze the charging and discharging characteristics of the energy storage system;

[0010] S3 Comprehensive access capacity assessment: Utilize the characteristic analysis module developed in S2 to analyze the development potential of renewable energy in the target area, access capacity assessment, future output and load forecast, equipment load limit determination, and grid weak link identification. The development potential assessment includes development potential assessment considering light resource endowment, development potential assessment considering geographical attribute constraints, and development potential assessment considering economic cost constraints.

[0011] 2. The method for evaluating the access capability of a multi-energy coordinated and complementary power grid according to claim 1 is characterized in that the access capability evaluation in the S3 access capability comprehensive evaluation includes the following contents:

[0012] a1. Draw the power grid topology diagram of the target area to be evaluated; then determine whether the total output of renewable energy in the target area is greater than the power load;

[0013] a2. Count the current status of short-circuit current and voltage deviation of each busbar at the current level and the actual measured value of harmonics, and check them against various limit values;

[0014] a3. Under the normal operation mode of the target area power grid to be evaluated, conduct thermal stability assessment to determine the reverse load rate of the current level transformers and lines and the renewable energy capacity that can be added;

[0015] a4. Calculate and verify short-circuit current, voltage deviation and harmonics based on the newly added renewable energy capacity;

[0016] a5. If the verification fails, gradually reduce the capacity of renewable energy that can be added, and repeat the steps until the verification passes. The capacity that passes the verification is the renewable energy access capacity of the current level of the target area power grid;

[0017] a6. After completing the calculation of the current voltage level of the power grid, compare the calculation result with the previous voltage level and the overall development potential of the target area based on the topological connection relationship, take the smaller value as the evaluation result of this level, and then gradually reduce the voltage level until the calculation of all voltage levels of the power grid in the target area is completed;

[0018] a7. Summarize the calculation results at all levels, divide the target area power grid renewable energy access capacity level, and list the renewable energy access capacity margin of each level of bus.

[0019] Furthermore, in the present invention, in the above step a1, the evaluation scope is divided according to the power supply area of ​​a single 220kV transformer, and the evaluation objects include all 35-220kV transformers and 35-110kV lines in the target area.

[0020] Furthermore, in the present invention, based on the access capability level obtained in step a7, improvement measures are proposed for weak links.

[0021] Furthermore, in the present invention, in the above step of S1 data collection and preprocessing, the load sample information includes historical power consumption data of different types of loads, seasonal variation patterns, daily load curves, and power consumption data of special event loads.

[0022] Furthermore, in the present invention, in the above-mentioned step of S1 data collection and preprocessing, the section operation sample information includes the load rate of the transmission line, the operating status of the substation, the network congestion situation, the stability index of the power system and the recovery capability of the power grid in an emergency.

[0023] Furthermore, in the present invention, in the above-mentioned S2 characteristic analysis module analysis step, the output characteristics include an output curve, a power fluctuation characteristic and a response speed.

[0024] Furthermore, in the present invention, in the above-mentioned S2 characteristic analysis module analysis step, the transmission capacity and operation characteristics include line capacity, transformer capacity, grid stability and scheduling flexibility.

[0025] Furthermore, in the present invention, in the above-mentioned S2 characteristic analysis module analysis step, the load characteristics include daily / weekly / monthly load change curves, seasonal change patterns and special event impacts.

[0026] Furthermore, in the present invention, in the above-mentioned S2 characteristic analysis module analysis step, the charge and discharge characteristics include energy storage capacity, charge and discharge rate, efficiency and cost-effectiveness.

[0027] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0028] By building a characteristic library and collecting different types of energy output samples and load samples, the ability of renewable energy to access the power grid can be comprehensively evaluated from multiple dimensions, which is more comprehensive than the traditional evaluation method that relies on a single data source. In addition, by predicting future output and load, it is helpful to plan the development path of renewable energy in advance and avoid problems such as unstable power grid operation caused by inaccurate predictions. By determining the upper limit of equipment load, it can help power grid managers better understand the actual bearing capacity of existing infrastructure, so as to reasonably arrange the investment and construction of renewable energy projects and avoid waste of resources. It can also provide scientific basis for decision makers by evaluating the development potential of renewable energy, which will help to formulate more reasonable policies and support measures and promote the healthy and rapid development of the renewable energy industry. By optimizing the access plan of renewable energy, the present invention is expected to reduce unnecessary energy loss and improve energy efficiency, thereby bringing economic benefits while also promoting environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0030] Figure 1 is an evaluation flow chart of the present invention;

[0031] Figure 2 It is a simulation model diagram of the present invention;

[0032] Figure 3 It is the output curve diagram of the present invention;

[0033] Figure 4 It is the load curve diagram of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the embodiments and drawings. The schematic implementation mode of the present invention and its description are only used to explain the present invention and are not intended to limit the present invention. The following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present invention.

[0035] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and limited, the terms "setting", "installation" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0036] Example 1

[0037] This embodiment 1 provides a method for evaluating the access capability of a power grid with coordinated and complementary multi-energy sources, including the following contents:

[0038] S1 Data collection and preprocessing: Build a renewable energy power generation facility characteristic database, which includes but is not limited to the historical output sample information of wind power, solar power and other renewable energy power generation facilities in the target area, collect and organize the power load sample information in the target area, ensure that the sample covers a sufficient time range to reflect seasonal changes and daily fluctuations, and summarize the cross-section operation sample information of the power grid operation status for subsequent analysis of power grid stability and its impact on renewable energy access; the characteristic database also includes weather data, market electricity price information, and power policy and regulatory factors in the target area;

[0039] Furthermore, the weather data in the characteristic database includes meteorological parameters such as temperature, humidity, wind speed, light intensity, and precipitation; the market electricity price information in the characteristic database includes historical electricity price data, predicted electricity price trends, and electricity price fluctuations; the electricity policies and regulations in the characteristic database include government support policies for renewable energy, emission standards, tax incentives, etc.;

[0040] Furthermore, the output sample information in the characteristic library includes, but is not limited to, power generation data and output curves of different types of energy (such as solar energy, wind energy, etc.), the unpredictable impact of intermittent energy, the availability of conventional energy, and their dynamic response characteristics;

[0041] The load sample information in the characteristic library covers the historical power consumption data of different types of loads (such as industrial loads, commercial loads, residential loads, etc.), seasonal variation patterns, daily load curves, and the impact of special events (such as holidays, sports events, etc.) on load power consumption data;

[0042] The section operation sample information in the characteristic library involves the operation status of the power grid in different time periods, including but not limited to the load rate of the transmission line, the operation status of the substation, the network congestion, the stability index of the power system and the recovery capability of the power grid in an emergency.

[0043] S2 characteristic analysis module analysis: Based on the characteristic library established in S1, analyze the output characteristics of different renewable energy sources; analyze the transmission capacity and operation characteristics of the power grid; analyze the load characteristics of different types of loads; analyze the charging and discharging characteristics of the energy storage system. It aims to deeply understand the output characteristics of various power sources and the changing laws of power demand in the region, and select representative cases for further research by simulating power output and load demand in different scenarios, so as to better adapt to the uncertainty factors in actual operation;

[0044] Furthermore, the S2 characteristic analysis module includes:

[0045] Analyze the output characteristics of different types of renewable energy sources (such as solar energy, wind energy, etc.), including output curves, power fluctuation characteristics, and response speed:

[0046] 1) Output curve

[0047] Photovoltaic: Usually uses solar radiation intensity (W / m 2 ) and the efficiency of the photovoltaic panels to estimate the power generation. The following formula can be used:

[0048] P PV =A·G·η (1)

[0049] Where: P PV is the output power of the PV system (kW), A is the area of ​​the PV array (m 2 ), G is the solar irradiance (W / m 2 ), η is the conversion efficiency.

[0050] Wind power: The output power of a wind turbine can be estimated by the following formula:

[0051] P w =0.5·ρ·A·v 3 ·C p (2)

[0052] 2) Power fluctuation characteristics

[0053] The standard deviation method is used to quantify the fluctuation of output power. Standard deviation is a commonly used statistical indicator to measure the degree of data dispersion. For a set of power output data P = {P1, P2, ..., P n}, its standard deviation σ can be calculated by the following formula:

[0054]

[0055] The power variation pattern is studied using the autocorrelation function ACF, which is a tool used to measure the linear correlation between a time series and its own lagged version. For a time series, the autocorrelation function of its lag is defined as:

[0056]

[0057] Where: C ov (X t ,X t-k ) is X t and X t-k The covariance between Yes X t The variance of .

[0058] The steps to calculate ACF are as follows:

[0059] Compute the mean μ of the original time series.

[0060] For each lag period k, calculate Xt and X t-k The covariance between .

[0061] Divide the covariance by the variance of the time series to get the ACF value ρ k .

[0062] ACF plots are usually drawn to visually display the correlations at different lags.

[0063] 3) Response speed

[0064] Natural energy sources such as wind and solar power have a slower response rate and are mainly dependent on changing weather conditions.

[0065] Analyze the transmission capacity and operating characteristics of the power grid, including line capacity, transformer capacity, grid stability and dispatch flexibility;

[0066] Analyze the load characteristics of different types of loads (such as industrial, commercial, residential loads, etc.), including daily / weekly / monthly load curves, seasonal variation patterns, and the impact of special events;

[0067] Analyze the charging and discharging characteristics of energy storage systems, including storage capacity, charging and discharging rate, efficiency, and cost-benefit analysis.

[0068] S3 comprehensive access capacity assessment, using the characteristic analysis module developed in S2 to analyze the development potential of renewable energy in the target area, access capacity assessment, future output and load forecasting, equipment load limit determination and grid weak link identification. The development potential assessment includes development potential assessment considering light resource endowment, development potential assessment considering geographical attribute constraints, and development potential assessment considering economic cost constraints. Determine the maximum tolerance of the power grid system for new installed capacity based on the existing infrastructure status and forecast results, identify potential weaknesses or bottlenecks in the current power grid, and propose improvement suggestions to support a higher proportion of clean energy access.

[0069] Furthermore, the above step S3 access capability comprehensive assessment includes:

[0070] Development potential assessment:

[0071] 1) Consider the evaluation of light resource endowment

[0072] One of the basic conditions for the development of photovoltaic renewable energy is that the annual solar radiation (GHI) in the area must be greater than a specific value. According to the current practice and experience of photovoltaic renewable energy construction at home and abroad, generally GHI greater than 1000kWh / m2 is worth developing. In areas that meet the GHI conditions, the theoretical exploitable amount of photovoltaic renewable energy can be calculated using the unit area method. The specific estimation formula is as follows:

[0073] E theory=S theory ×W (5)

[0074] Where: E theory is the theoretical development volume of renewable energy power stations in the region; S theory is the theoretically installable area of ​​renewable energy power stations in the region; W is the capacity of renewable energy power stations that can be installed per unit area in the region, which can generally be taken as 30w / m2.

[0075] 2) Evaluation considering geographic attribute constraints

[0076] On the basis of the theoretical developable area, the area of ​​undevelopable and restricted areas is further eliminated. The specific estimation formula is as follows:

[0077] E technology =T technology ×W (6)

[0078] T technology =S theory -NF (7)

[0079] Where: E technology is the amount of technology development of renewable energy power stations in the region; T technology It is the technically installable area of ​​renewable energy power stations in the region; N and F are the areas that cannot be developed and the areas with restricted development, respectively.

[0080]

[0081] Where N i It is the i-th type of undevelopable area, such as water bodies, wetlands, swamps, nature reserves, historical sites, minerals, etc.

[0082]

[0083] In the formula, F t The development area of ​​the tth restricted type, including farmland, grassland, woodland, shrubs, etc. t The usable area ratio of the development area of ​​the tth restriction type is determined according to the literature. The exploitable areas of renewable energy resources in different land use regions are: 80% for farmland and grassland, 20% for forest and 65% for bush.

[0084] 3) Evaluation considering economic cost constraints

[0085] The results of the theoretical and technical exploitable amount assessment of renewable energy power stations are generally large, but due to the influence of factors such as the development cost, electricity demand, and electricity price level of the year, most renewable energy power station bases do not have economic value for development. Therefore, it is necessary to further calculate the economic exploitable amount of renewable energy power station bases. The project uses the levelized cost of electricity (LCOE) to characterize the economic feasibility of different power station bases.

[0086] In the technology can develop T technology In the region, considering the operation and maintenance costs, transmission costs and other factors in different regions, T technology Divided into k sub-regions T k , calculate each T k The LCOE can be compared with the local average on-grid electricity price level of the year, or with the local average on-grid electricity price of thermal power in the year, or with the average on-grid electricity price of external transmission. If the LCOE is less than the given standard, the renewable energy power station in the area has economically viable development value. The specific calculation formula is as follows:

[0087] E economic =Σ k T k ×w (10)

[0088]

[0089] In the formula, E economic The economically exploitable amount of renewable energy power plants in the region; T is the nth year k The electricity cost calculated by the region; n,local_grid , P n,local_thermal , P n,cross_province They are respectively the average on-grid electricity price of the local power grid, the average on-grid electricity price of thermal power and the average on-grid electricity price of transmission in the year.

[0090] LCOE is an internationally accepted indicator for evaluating the cost of 1 kWh of electricity. It converts the initial investment and operating costs during the project life cycle into present value. The definition of LCOE is to make the net present value of total costs equal to the net present value of total revenues. The specific calculation formula is as follows:

[0091]

[0092] Where: C n is the total cost in year n; Q n is the power generation in the nth year; B n is the subsidy income for renewable energy power generation; i is the discount rate; N is the operating years; LCOE n is the electricity cost per kilowatt-hour in the nth year.

[0093] Through formula conversion, we get the LCOE expression formula:

[0094]

[0095] It can be seen from the formula that subsidy B n , discount rate i, and operating period N can be basically determined. The factor that actually affects the economic feasibility of the project is the development cost C n and power generation Q n The main factors affecting development costs are the initial investment C1, the subsequent C2 to C n The operating costs, depreciation and amortization tend to be basically stable year by year, while the installation engineering costs and land acquisition costs are the factors with the largest changes in the initial investment C1, while the equipment purchase costs vary less. n It is mainly affected by the number of hours of power generation in the location.

[0096] Output and load forecast:

[0097] 1) Output prediction

[0098] First, based on the above prediction of renewable energy development potential, combined with the analysis of output characteristics in spring, summer, autumn and winter, the typical daily renewable energy generation curves in different seasons are obtained through standardization. Then, the typical daily curve is smoothed to form an ideal output coefficient curve; finally, the maximum output coefficient of renewable energy on typical days in each quarter is selected as the base for renewable energy output prediction and multiplied by the development volume considering economic costs to obtain the renewable energy output prediction result.

[0099] 2) Load forecasting

[0100] Based on relevant information such as historical load data and capacity requirements reported by large users, combined with the forecast results of electricity consumption in the whole society, no less than 3 appropriate forecasting methods are selected to forecast power load, and high, medium and low plans are proposed. After comprehensive analysis, a recommended plan is given.

[0101] Access capability assessment:

[0102] 1) Evaluation Method

[0103] Thermal stability assessment: refers to the assessment of the additional capacity margin of renewable energy based on the load timing of transformers and lines, with the principle that the thermal stability of transformers or lines does not exceed the limit.

[0104] 220kV: Considering the principle that distributed power sources in renewable energy cannot feed back to the 220kV and above main grid, the renewable energy capacity that can be connected to the 220kV transformer is equal to the net load of the substation (P L -P D ).

[0105] 110kV-35kV: Calculate the reverse load rate λ of the transformer and line, and evaluate the renewable energy capacity P that can be added to the transformer or line m .

[0106]

[0107] P m =(0.8-λ max )×S e ×k r (15)

[0108] Where: P D Contribute to renewable energy, P L is the equivalent power load at the same moment, that is, the load minus the output of other power sources except renewable energy; S e is the actual operating limit of the transformer or line; k r is the equipment operating margin factor, k r ≤1.

[0109] Short-circuit current verification: After renewable energy is connected, the short-circuit current of the busbars at all levels of the system shall not exceed the short-circuit current limit in principle.

[0110] After the renewable energy to be verified (Pm) is connected, the verification is carried out according to the following formula:

[0111]

[0112] Where: I xz U is the current value of the busbar short-circuit current under the maximum operation mode of the system, which can be determined according to the busbar maximum mode short-circuit capacity; N I is the rated voltage of each busbar. The short-circuit current generated by renewable energy usually does not exceed 1.5 times of its rated current. m For the allowable short-circuit current limit, the minimum value of the breaking current limit of the corresponding circuit breaker connected to the busbar should be selected.

[0113] Voltage deviation verification: The principle is that the grid voltage does not exceed the limit before renewable energy is connected.

[0114] According to the voltage deviation limit specified in the national standard "Power Quality Supply Voltage Deviation", the allowable positive and negative voltage deviations ΔU+ and ΔU- caused by the access of renewable energy are estimated and verified according to the following formula:

[0115] δU + <ΔU + And δU - >ΔU - (17)

[0116] Where: δU + , δU -It is the extreme value of positive and negative voltage deviation calculated by the formula.

[0117] Harmonic verification: The principle is that the harmonic current value and interharmonic voltage content rate of the nodes where renewable energy is connected to the grid do not exceed the limit.

[0118] The harmonic current is checked according to the following formula:

[0119] I XZ,h <I h (18)

[0120] Where: I xz,h is the measured value of the hth harmonic current of the power grid node; I h It is the allowable value of the hth harmonic current injected into the grid nodes of each voltage level.

[0121] The interharmonic voltage content rate is checked according to the following formula:

[0122]

[0123] Where: I HRUxz,i is the measured value of the interharmonic voltage content rate of the grid node i; I HRUi It is the limit value of the i-th interharmonic voltage content rate at each voltage level.

[0124] 2) The evaluation process of access capability evaluation is as follows: Figure 1 As shown, the details are as follows:

[0125] a1. Define the scope of the target area power grid to be evaluated and draw the target area power grid topology map. The evaluation scope is divided into the power supply area of ​​a single 220kV transformer, and the evaluation objects include all 35~220kV transformers and 35~110kV lines in the area.

[0126] Determine whether there is any reverse transmission of renewable energy to the 220kV and above power grid in the target area to be evaluated, that is, whether the total output of renewable energy in the area is greater than the power load. If reverse transmission occurs, the renewable energy access capacity level of each voltage level in the evaluation area will be red.

[0127] a2. Count the current status and measured values ​​of the short-circuit current and voltage deviation of each busbar at the current level, and verify them with reference to the various limit values. If the verification fails, the renewable energy access capacity level of the regional power grid at or below this voltage level will be red.

[0128] a3. Under the normal operation mode of the target area power grid to be evaluated, conduct a thermal stability assessment to determine the reverse load rate of the current level transformers and lines and the renewable energy capacity that can be added. If the reverse load rate is greater than 80%, the renewable energy access capacity level of the regional power grid at or below the voltage level is red.

[0129] a4. Calculate and verify short-circuit current, voltage deviation and harmonics based on the newly added renewable energy capacity.

[0130] a5. If the verification fails, gradually reduce the capacity of renewable energy that can be added, and repeat the steps until the verification passes. The capacity that passes the verification is the renewable energy access capacity of the current level of the power grid to be evaluated.

[0131] a6. After completing the calculation of the current voltage level grid, compare the calculation result with the previous voltage level and the overall development potential of the region based on the topological connection relationship, and take the smaller value as the evaluation result of this level. Then, gradually reduce the voltage level until the calculation of all voltage levels of the grid to be evaluated is completed.

[0132] a7. Summarize the calculation results at all levels, classify the renewable energy access capacity of the power grid to be evaluated, and list the renewable energy access capacity margin of the busbars at all levels.

[0133] The evaluation levels are as shown in the following table:

[0134]

[0135] Weakness analysis:

[0136] Based on the access capability assessment results, identify weak links and propose improvement measures, specifically:

[0137] 1) Identify transformers and lines with insufficient margin in the grid structure through the reverse load rate. The reverse load rate is used to measure the ratio between the actual load of the equipment (such as transformers or lines) and its rated capacity. Usually, we hope that this ratio will be kept within a reasonable range to ensure that the equipment is not overloaded. The reverse load rate calculation formula has been given in the access capacity assessment:

[0138]

[0139] 2) Identify devices with failed protection during grid operation through short-circuit current. Short-circuit current refers to the current flowing through the protection device when a short-circuit fault occurs in the power system. By analyzing the short-circuit current, it is possible to evaluate whether the protection device can operate effectively.

[0140] The short-circuit current is calculated as follows:

[0141]

[0142] Where: I sc is the short-circuit current, V th is the open circuit voltage of the equivalent voltage source, Z sys It is the total impedance of the system from the power supply to the short-circuit point.

[0143] 3) Identify voltage-exceeding devices during grid operation through node voltage, which is usually obtained through power flow calculation.

[0144] 4) Identify equipment with excessive power quality during grid operation through the content of harmonic current and interharmonic voltage. Harmonic current and interharmonic voltage reflect the state of power quality.

[0145] The calculation formula for harmonic current and interharmonic voltage content is as follows:

[0146]

[0147] Where: I n and U k They represent the nth harmonic current and harmonic / interharmonic voltage respectively, I1 and U1 are the fundamental current and fundamental voltage, and N is the highest harmonic order considered.

[0148] Example 2

[0149] The power grid overview of this embodiment:

[0150] The assessment scope of this embodiment includes 1 220kV transformer with a transformer capacity of 150MVA, 3 110kV transformers with a transformer capacity of 40MVA, and 2 35kV transformers with a transformer capacity of 10MVA. There is no centralized power supply access within the assessment scope, and the current annual access to 8.33MW of new energy power.

[0151] The time period when grid power flow is most likely to reverse is selected, that is, the time period when power load demand is low and photovoltaic output is large. Combined with the regional load characteristics and photovoltaic output characteristics, it is generally spring noon. Therefore, spring noon is selected for reverse load rate evaluation, and its load is 50% of the maximum load in summer.

[0152] The power flow, short circuit and harmonics are simulated and calculated by ETAP software, and the calculation period is 2025. The new energy grid connection method is to connect to the 10kV busbar of the transformer (actually, multiple lines are connected to the busbar, and for clear display, the example will merge them into one); considering that it is a distributed access, only the carrying capacity of each substation is calculated, and the line transmission capacity is used as a check, and the smaller value of the substation capacity is included in the calculation. The simulation model is as follows Figure 2 shown.

[0153] Development potential assessment:

[0154] 1) Consider the evaluation of light resource endowment

[0155] By developing photovoltaic power in an area where the annual solar radiation (GHI) is greater than 1000kWh / m2, the development potential considering resource endowment constraints is obtained. The evaluation results considering the light resource endowment are shown in the following table.

[0156] Area Statistics Total area <![CDATA[Photovoltaic development area (km 2 )]]> Developable photovoltaic capacity (MW) Agricultural land 34.94 3.49 104.7 Construction land 6.8 2.12 63.6 total 41.74 5.61 168.3

[0157] 2) Evaluation considering geographic attribute constraints

[0158] By eliminating the undevelopable area and restricted development area, the development potential considering the geographical attribute constraints is obtained. The evaluation results considering the geographical attribute constraints are shown in the following table.

[0159] Area Statistics Total area <![CDATA[New energy development area (km 2 )]]> Developable amount of new energy (MW) Agricultural land 34.94 1.6 48 Construction land 6.8 0.54 16.2 total 41.74 10.16 64.2

[0160] 3) Evaluation considering economic cost constraints

[0161] According to the grid-connected electricity price, electricity market transaction price and electricity transmission situation, the development potential considering economic cost constraints is obtained. The evaluation results considering economic cost constraints are shown in the following table.

[0162] Area statistics Total area <![CDATA[New energy development area (km 2 )]]> Developable amount of new energy (MW) Agricultural land 34.94 0.4 12 Construction land 6.8 0.25 7.5 total 41.74 0.65 19.5

[0163] Output load forecast:

[0164] Combined with the exploitable potential of new energy and its operating characteristics, the photovoltaic output in the future is predicted; combined with conventional load forecasting methods, the load in the future is predicted.

[0165] 1) Output prediction

[0166] By 2025, the total installed capacity of photovoltaic power generation will be 19.5MW (based on the economic cost assessment results). Combined with the new energy output analysis and scenario setting, the maximum output coefficient in spring and autumn is taken as 1.0. The output curve is as follows: Figure 3 The output prediction results are shown in the following table.

[0167]

[0168] 2) Load forecasting

[0169] Combined with the forecast results of electricity consumption in the whole society, the regression analysis method, natural growth + large user method and annual maximum load utilization hours method are selected to predict the load of a certain place in 2025. The load curve is as follows: Figure 4 The load forecast results are shown in the following table.

[0170]

[0171] New energy access capability assessment:

[0172] The calculation and evaluation results are shown in the following table. When new energy is connected to a single substation, the maximum capacity that can be connected is "the new capacity that can be added after top-down evaluation". When new energy is connected to multiple substations, in addition to the capacity constraint of a single substation, the total amount connected to the area cannot exceed the smaller value of the regional development potential of 19.5MW and the new capacity of the 220kV substation.

[0173]

[0174]

[0175] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the access capability of a power grid with multi-energy synergy and complementation, characterized in that: Include the following: S1 Data collection and preprocessing: Build a renewable energy power generation facility characteristic database, which includes output sample information, load sample information, and cross-section operation sample information of renewable energy type power generation facilities in the target area that summarizes the grid operation status. It also includes weather data, market electricity price information, and power policy and regulatory factors in the target area. S2 characteristic analysis module analysis: Based on the characteristic library established in S1, analyze the output characteristics of different renewable energy sources; analyze the transmission capacity and operation characteristics of the power grid; analyze the load characteristics of different types of loads; analyze the charging and discharging characteristics of the energy storage system; S3 Comprehensive Access Capacity Assessment: Utilize the characteristic analysis module developed in S2 to analyze the development potential of renewable energy in the target area, access capacity assessment, future output and load forecast, equipment load limit determination, and grid weak link identification. The development potential assessment includes development potential assessment considering light resource endowment, development potential assessment considering geographical attribute constraints, and development potential assessment considering economic cost constraints.

2. The method for evaluating the access capability of a multi-energy grid with coordinated complementarity according to claim 1 is characterized in that: The access capability assessment in the S3 access capability comprehensive assessment includes the following: a1. Draw the power grid topology diagram of the target area to be evaluated; then determine whether the total output of renewable energy in the target area is greater than the power load; a2. Count the current status of short-circuit current and voltage deviation of each busbar at the current level and the actual measured value of harmonics, and check them against various limit values; a3. Under the normal operation mode of the target area power grid to be evaluated, conduct thermal stability assessment to determine the reverse load rate of the current level transformers and lines and the renewable energy capacity that can be added; a4. Calculate and verify short-circuit current, voltage deviation and harmonics based on the newly added renewable energy capacity; a5. If the verification fails, gradually reduce the capacity of renewable energy that can be added, and repeat the steps until the verification passes. The capacity that passes the verification is the renewable energy access capacity of the current level of the target area power grid; a6. After completing the calculation of the current voltage level of the power grid, compare the calculation result with the previous voltage level and the overall development potential of the target area based on the topological connection relationship, take the smaller value as the evaluation result of this level, and then gradually reduce the voltage level until the calculation of all voltage levels of the power grid in the target area is completed; a7. Summarize the calculation results at all levels, divide the target area power grid renewable energy access capacity level, and list the renewable energy access capacity margin of each level of bus.

3. The method for evaluating the access capability of a power grid with multi-energy synergy and complementarity according to claim 2 is characterized in that: In step a1, the assessment scope is divided according to the power supply area of ​​a single 220kV transformer, and the assessment objects include all 35-220kV transformers and 35-110kV lines in the target area.

4. The method for evaluating the access capability of a power grid with multi-energy synergy and complementarity according to claim 2 is characterized in that: Based on the access capability level obtained in step a7, propose improvement measures for weak links.

5. The method for evaluating the access capability of a multi-energy coordinated and complementary power grid according to claim 1 is characterized in that: In the step of S1 data collection and preprocessing, the load sample information includes historical power consumption data of different types of loads, seasonal variation patterns, daily load curves, and power consumption data of special event loads.

6. The method for evaluating the access capability of a multi-energy coordinated and complementary power grid according to claim 1 is characterized in that: In the S1 data collection and preprocessing step, the section operation sample information includes the load rate of the transmission line, the operating status of the substation, the network congestion, the stability index of the power system and the recovery capability of the power grid in an emergency.

7. The method for evaluating the access capability of a power grid with multi-energy synergy and complementarity according to claim 1 is characterized in that: In the S2 characteristic analysis module analysis step, the output characteristics include output curve, power fluctuation characteristics and response speed.

8. The method for evaluating the access capability of a power grid with multi-energy synergy and complementarity according to claim 1 is characterized in that: In the S2 characteristic analysis module analysis step, the transmission capacity and operation characteristics include line capacity, transformer capacity, grid stability and dispatching flexibility.

9. The method for evaluating the access capability of a multi-energy coordinated and complementary power grid according to claim 1 is characterized in that: In the S2 characteristic analysis module analysis step, the load characteristics include daily / weekly / monthly load change curves, seasonal change patterns and the impact of special events.

10. The method for evaluating the access capability of a multi-energy coordinated and complementary power grid according to claim 1, characterized in that: In the S2 characteristic analysis module analysis step, the charge and discharge characteristics include energy storage capacity, charge and discharge rate, efficiency and cost-effectiveness.

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