Power grid clean energy consumption early warning method and system based on multi-source indexes

Abstract

The invention discloses a multi-source index-based power grid clean energy consumption early warning method and system. The method comprises the steps of collecting historical operation data of a power grid and prediction data of an operation day to be evaluated; analyzing the internal relationship between the electric quantity adequacy and peak regulation and wind, light and water curtailment, constructing a fitting function, and evaluating and predicting the interval peak regulation demand; and selecting a typical operation day, evaluating the peak regulation capability, and issuing clean energy consumption early warning according to the difference between the peak regulation demand and the peak regulation capability. According to the method, the expected peak regulation demand evaluation method based on the electric quantity adequacy is constructed, and the expected peak regulation capacity in the expected interval is analyzed and calculated through the typical operation day. And by comparing the peak regulation demand with the peak regulation capability, evaluating and calculating the wind and light abandoning and water abandoning electric quantity of each operation day in the prediction interval. And comprehensively considering generation conditions of abandoned wind, abandoned light and abandoned water of each operation day in the prediction interval, and issuing clean energy consumption early warning.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to a method and system for early warning of clean energy consumption in a power grid based on multi-source indicators. Background Art

[0002] Clean energy consumption early warning is an important technology in power grid dispatching and operation. Its function is to warn of clean energy consumption risks based on clean energy consumption demand and power grid consumption capacity through electricity supply and demand situation assessment.

[0003] Traditional power grid clean energy early warning mainly adopts the peak-shaving capacity assessment method, that is, to evaluate the peak-shaving margin of the power grid on a certain operating day to determine whether it meets the clean energy consumption requirements. In essence, it evaluates the peak-shaving situation at the moment when the peak-shaving pressure is the greatest on a single operating day, which serves as the basis for issuing clean energy consumption early warnings.

[0004] However, clean energy consumption is an analysis of the amount of power abandoned by wind, solar and hydropower. The power abandoned by wind, solar and hydropower in a single period cannot reflect the overall clean energy consumption situation, resulting in the following problems in traditional early warning methods:

[0005] First, it cannot reflect the overall clean energy consumption situation. Traditional early warning methods can only reflect the clean energy consumption situation on a single operating day and cannot effectively warn the overall consumption situation.

[0006] Second, it cannot truly reflect the expected wind, solar and water abandonment situations under the difficulty of clean energy consumption. The wind, solar and water abandonment power in a single period cannot reflect the overall level of wind, solar and water abandonment. Summary of the invention

[0007] In view of the above-mentioned problems, the present invention is proposed.

[0008] Therefore, the technical problem solved by the present invention is that the traditional method cannot reflect the overall clean energy consumption situation, and cannot truly reflect the expected wind, solar and water abandonment situations when clean energy consumption is difficult.

[0009] To solve the above technical problems, the present invention provides the following technical solutions: a method for early warning of clean energy consumption in a power grid based on multi-source indicators, comprising: collecting historical operation data of the power grid and forecast data of the operation day to be evaluated; analyzing the intrinsic relationship between power surplus and peak regulation, wind abandonment, solar abandonment and water abandonment, constructing a fitting function, and evaluating the peak regulation demand in the prediction interval; selecting a typical operation day, evaluating the peak regulation capacity, and issuing a clean energy consumption early warning based on the difference between the peak regulation demand and the peak regulation capacity.

[0010] As an optimal solution of the power grid clean energy consumption early warning method based on multi-source indicators described in the present invention, wherein: the historical operation data of the power grid includes the power load of historical operation days, clean energy power generation forecast, the upper and lower limits of conventional power generation output, and the amount of abandoned wind, solar and hydropower, which are derived from the power grid energy management system; the forecast data for the operation day to be evaluated includes the power load, clean energy power generation forecast, the upper and lower limits of conventional power generation output and peak-shaving capacity, which are derived from the power generation plan management system.

[0011] As a preferred solution of the power grid clean energy consumption early warning method based on multi-source indicators described in the present invention, wherein: analyzing the intrinsic relationship between the power sufficiency and peak load regulation, wind abandonment, solar abandonment and hydropower abandonment includes that the power sufficiency index of any operating day is defined as the ratio of the conventional power generation demand determined by the predicted power consumption and clean energy power generation demand to the minimum power generation corresponding to its minimum technical output, expressed as:

[0012]

[0013] in, Indicates the power abundance index on the operating day; It represents the demand for conventional power generation determined by the predicted power consumption and clean energy power generation demand; Indicates the minimum power generation corresponding to the minimum technical output of conventional power sources; the power generation demand of conventional power sources determined by the predicted power consumption and clean energy power generation demand It is equal to the power generation demand calculated based on the predicted power consumption minus the predicted clean energy power generation, expressed as:

[0014]

[0015] in, represents the predicted power consumption on the operation day d; η represents the network loss rate; represents the predicted power generation of clean energy source n on operating day d; NN represents the number of clean energy power stations.

[0016] As a preferred solution of the power grid clean energy consumption early warning method based on multi-source indicators described in the present invention, the fitting function includes counting the peak-shaving power of conventional power sources and the abandoned wind, solar and hydropower on each operating day, and fitting to generate a peak-shaving demand-power sufficiency function, which is expressed as:

[0017]

[0018] In the formula, represents the peak-shaving demand of the operating day of the fitted production; a PR represents the coefficient of the linear term in the linear function fitting function; b PR Represents the constant term coefficient in the linear function fitting function; λ EmaxIt represents the maximum value of the power surplus corresponding to the operation day with peak load regulation and abandoned wind, solar and hydropower calculated from historical data; Emin It indicates the minimum value of electricity surplus corresponding to the operating day with peak-shaving electricity and abandoned wind, solar and hydroelectric power, calculated based on historical data.

[0019] As a preferred solution of the power grid clean energy consumption early warning method based on multi-source indicators described in the present invention, wherein: evaluating the peak-shaving demand in the forecast interval includes taking the clean energy power and power consumption forecast accuracy as the judgment criteria for the forecast interval, expressed as:

[0020]

[0021] In the formula, ε N,set Represents the clean energy power forecast accuracy limit; ε P,set Indicates the accuracy limit of electricity consumption prediction; Indicates the prediction accuracy of clean energy power on the operating day; It indicates the prediction accuracy of clean energy electricity on the operation day d+1; Indicates the prediction accuracy of power consumption on operation day d; It indicates the prediction accuracy of electricity consumption on the operating day d+1. It calculates the electricity surplus of each operating day within the prediction interval, and predicts the peak load demand of each operating day within the prediction interval through the fitting function.

[0022] As a preferred solution of the power grid clean energy consumption early warning method based on multi-source indicators described in the present invention, wherein: the selection of typical operating days and the evaluation of peak load capacity include determining typical operating days within the prediction interval, and constructing a multi-period optimization simulation model to evaluate the peak load capacity that conventional power sources can provide on typical operating days. The multi-period optimization simulation model is expressed as:

[0023]

[0024] Among them, NonGN represents the non-adjustable clean energy set; GN represents the adjustable clean energy set; It represents the abandoned wind and solar power of non-adjustable clean energy in period n and t; It indicates that the amount of water power abandoned by adjustable clean energy n; It represents the power output of conventional power source in period c and t; It represents the power generation output of clean energy in period n and t; Indicates the power load during time period t; Indicates the maximum technical output of conventional power source c; It indicates the minimum output corresponding to the maximum peak-shaving capacity provided by conventional power source c; It represents the predicted power generation output of non-adjustable clean energy in period n and t; represents the maximum technical output of adjustable clean energy n; represents the minimum technical output of adjustable clean energy n; represents the maximum power generation of adjustable clean energy n; represents the minimum power generation of the adjustable clean energy n; NT represents the number of time periods; ΔT represents the time interval; NC represents the number of conventional power sources;

[0025] The peak load regulation capacity of conventional power sources on a typical operating day is the peak load regulation power corresponding to the output of conventional power sources on a typical operating day being lower than the minimum technical output, expressed as:

[0026]

[0027] Among them, E A,PR Indicates the peak load regulation capability of conventional power supply in a typical operating day; Indicates the minimum technical output of conventional power supply c.

[0028] As a preferred solution of the power grid clean energy consumption early warning method based on multi-source indicators described in the present invention, the clean energy consumption early warning includes predicting the peak-shaving situation, evaluating the abandoned wind, solar and hydropower, and issuing a clean energy consumption early warning according to the peak-shaving demand of each operating day in the forecast interval and the peak-shaving capacity of the forecast interval, and the expected abandoned wind, solar and hydropower on any operating day in the forecast interval is the difference between the peak-shaving demand and the peak-shaving capacity, which is expressed as:

[0029]

[0030] in, It represents the expected amount of wind, solar and hydropower abandoned on operating day d in the forecast interval; if the number of operating days with expected abandoned wind, solar and hydropower exceeds the limit, a red warning will be issued; if there are operating days with expected abandoned wind, solar and hydropower, but the total number of days does not exceed the limit, an orange warning will be issued; if the expected abandoned wind, solar and hydropower on each operating day in the forecast interval is 0, but there is peak load demand on each operating day, a yellow warning will be issued.

[0031] In the second aspect, the present invention also provides a power grid clean energy consumption early warning system based on multi-source indicators, including a data acquisition module, which collects historical power grid operation data through a power grid energy management system, and collects forecast data for the operation day to be evaluated through a power generation plan management system; a function construction module, which analyzes the intrinsic relationship between power supply abundance and peak regulation, wind power abandonment, solar power abandonment and hydropower abandonment, and counts the peak regulation power of conventional power sources and the wind power abandonment, solar power abandonment and hydropower abandonment on each operation day, and fits and generates a peak regulation demand-power supply abundance function to evaluate the peak regulation demand in the prediction interval; an early warning module, which determines a typical operation day within the prediction interval, and constructs a multi-period optimization simulation model to evaluate the peak regulation capacity, and issues a clean energy consumption early warning based on the difference between the peak regulation demand and the peak regulation capacity.

[0032] In a third aspect, the present invention further provides a computing device, comprising: a memory and a processor;

[0033] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the power grid clean energy consumption early warning method based on multi-source indicators are implemented.

[0034] In a fourth aspect, the present invention further provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for early warning of clean energy consumption in a power grid based on multi-source indicators.

[0035] Beneficial effects of the present invention: The present invention constructs an expected peak-shaving demand assessment method based on power abundance, and calculates the expected peak-shaving capacity within the expected interval through typical operation day analysis. By comparing the peak-shaving demand and peak-shaving capacity, the amount of wind, solar, and hydropower abandoned on each operating day in the forecast interval is evaluated and calculated. The occurrence of wind, solar, and hydropower abandoned on each operating day in the forecast interval is comprehensively considered, and a clean energy consumption warning is issued. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0037] Figure 1 An overall flow chart of a power grid clean energy consumption early warning method based on multi-source indicators provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0039] Example 1

[0040] Reference Figure 1 , which is an embodiment of the present invention, provides a power grid clean energy consumption early warning method based on multi-source indicators, comprising:

[0041] S1: Collect historical grid operation data and forecast data for the operation day to be evaluated.

[0042] Furthermore, the historical operation data of the power grid includes historical daily electricity load, clean energy power generation forecast, upper and lower limits of conventional power generation output, and the amount of wind, solar and hydropower abandoned, which come from the power grid energy management system.

[0043] The forecast data for the operating day to be evaluated includes electricity load, clean energy power generation forecast, upper and lower limits of conventional power output and peak-shaving capacity, which are derived from the power generation planning management system.

[0044] It should be noted that the clean energy power generation forecast includes power generation forecast data for renewable energy sources such as wind power, photovoltaics, and hydropower; the amount of abandoned wind, photovoltaic, and hydropower refers to the wind, solar, and hydropower generation that cannot be effectively utilized due to various reasons (such as insufficient grid load or grid capacity limitations), reflecting the waste in the utilization of clean energy.

[0045] In the traditional power grid operation mode, the utilization rate of clean energy is often limited by the peak-shaving capacity, resulting in a large amount of wind, solar and hydropower abandonment. The present invention analyzes the clean energy power generation forecast data, evaluates its power generation capacity on different operating days, and combines the peak-shaving capacity of conventional power sources to optimize the utilization rate of clean energy and reduce energy waste.

[0046] S2: Analyze the intrinsic relationship between power surplus and peak load regulation, wind power abandonment, solar power abandonment and hydropower abandonment, construct a fitting function, and evaluate the peak load regulation demand in the forecast period.

[0047] Furthermore, based on the historical data of electricity supply and demand, the intrinsic relationship between the electricity surplus index and the amount of wind, solar, and hydropower abandoned or peak-shaving electricity is analyzed as a warning value for electricity surplus.

[0048] The power adequacy index for any operating day is defined as the ratio of the power generation demand of conventional power sources determined by the predicted power consumption and clean energy power generation demand to the minimum power generation corresponding to its minimum technical output, expressed as:

[0049]

[0050] in, Indicates the power abundance index on the operating day; It represents the demand for conventional power generation determined by the predicted power consumption and clean energy power generation demand; Indicates the minimum power generation corresponding to the minimum technical output of conventional power sources.

[0051] Conventional power generation demand determined by predicted power consumption and clean energy generation demand It is equal to the power generation demand calculated based on the predicted power consumption minus the predicted clean energy power generation, expressed as:

[0052]

[0053] in, represents the predicted power consumption on the operation day d; η represents the network loss rate; represents the predicted power generation of clean energy source n on operating day d; NN represents the number of clean energy power stations.

[0054] The selection criteria for predicted power generation of different types of clean energy are different, and the requirements are as follows:

[0055] (1) For non-adjustable clean energy such as wind power and photovoltaic power, the predicted power generation can be used.

[0056] (2) For adjustable clean energy such as hydropower, the power generation is in an adjustable range. When evaluating the power adequacy, the minimum power generation should be used to make room for conventional power generation. The power generation range of the hydropower station is determined by its water inflow forecast and the overall upstream and downstream water level control, which is obtained by the hydropower dispatch analysis and serves as the boundary condition for the power dispatch evaluation analysis.

[0057] The minimum power generation corresponding to the minimum technical output of conventional power sources is equal to the sum of the daily power generation corresponding to the minimum technical output of all conventional power sources on that operating day, which can be expressed as:

[0058]

[0059] in, It represents the minimum technical output of the operating day, conventional power source, and c; NT represents the number of time periods; ΔT represents the time interval; and NC represents the number of conventional power sources.

[0060] The overall power surplus is positively correlated with the clean energy consumption. That is, the greater the power surplus, the greater the demand for conventional power generation determined by electricity demand and clean energy exceeds the corresponding power generation of the minimum technical output of conventional electricity, and the conventional power source will have a larger adjustment range to adapt to the clean energy consumption regulation requirements in each period; otherwise, the adjustment space will be more limited, which is not conducive to clean energy consumption.

[0061] Furthermore, the peak load of conventional power sources and the abandoned wind, solar, and hydropower on each operating day are counted, and the peak load demand-power sufficiency function is fitted. The sum of the peak load, abandoned wind, solar, and hydropower is the peak load demand required to meet the clean energy consumption of the power grid. The daily peak load demand is counted and analyzed with the power sufficiency to fit the peak load demand-power sufficiency function, which can be expressed as:

[0062]

[0063] In the formula, represents the peak-shaving demand of the operating day of the fitted production; a PR represents the coefficient of the linear term in the linear function fitting function; b PRRepresents the constant term coefficient in the linear function fitting function; λ Emax It represents the maximum value of the power surplus corresponding to the operation day with peak load regulation and abandoned wind, solar and hydropower calculated from historical data; Emin It indicates the minimum value of electricity surplus corresponding to the operating day with peak-shaving electricity and abandoned wind, solar and hydroelectric power, calculated based on historical data.

[0064] Furthermore, the clean energy consumption early warning assessment interval is determined, and the calculation formula is used to measure the power sufficiency of each operating day within the forecast interval. Unlike the traditional clean energy consumption early warning which only targets a single operating day, the present invention will conduct a trend analysis on the clean energy consumption of multiple consecutive operating days. The basis of the analysis is that the prediction accuracy of basic data such as clean energy power generation and power consumption on subsequent operating days meets the requirements.

[0065] The prediction accuracy of clean energy power and power consumption is used as the criterion for the prediction interval. The prediction interval requires:

[0066] (1) The prediction accuracy of clean energy electricity and electricity consumption within the prediction interval shall not be lower than the accuracy limit.

[0067] (2) After the forecast interval is extended by one day, the forecast accuracy of clean energy electricity and electricity consumption will be lower than the accuracy limit.

[0068] The judgment criteria can be expressed as:

[0069]

[0070] In the formula, ε N,set Represents the clean energy power forecast accuracy limit; ε P,set Indicates the accuracy limit of electricity consumption prediction; Indicates the prediction accuracy of clean energy power on the operating day; It indicates the prediction accuracy of clean energy electricity on the operation day d+1; Indicates the prediction accuracy of power consumption on operation day d; It indicates the prediction accuracy of electricity consumption on the operating day d+1. It calculates the electricity surplus of each operating day within the prediction interval, and predicts the peak-shaving demand of each operating day within the prediction interval through the peak-shaving demand-electricity surplus function.

[0071] S3: Select a typical operating day, evaluate the peak-shaving capacity, and issue a clean energy consumption warning based on the difference between peak-shaving demand and peak-shaving capacity.

[0072] Furthermore, within the forecast interval, a typical operating day is determined, and a multi-period optimization simulation model is constructed to evaluate the peak load regulation capacity that conventional power sources can provide on a typical operating day. The boundary data of power load, clean energy power generation, etc. on a typical operating day are specified as follows:

[0073] (1) The power load is defined as the average of the predicted power loads on each operating day within the prediction interval.

[0074] (2) The power generation curve of non-adjustable clean energy such as wind power and photovoltaic power is defined as the average of the predicted power generation curves for each operating day within the prediction interval.

[0075] (3) The power generation of adjustable clean energy such as hydropower is defined as a subset of the power generation range of each operating day within the prediction interval.

[0076] An optimization model is constructed to maximize the consumption of clean energy, with the goal of minimizing the amount of abandoned wind, solar and hydropower as the optimization target, and considering power balance, conventional power generation output constraints, and clean energy power generation output constraints. The multi-period optimization simulation model is expressed as:

[0077]

[0078]

[0079] Among them, NonGN represents the non-adjustable clean energy set; GN represents the adjustable clean energy set; It represents the abandoned wind and solar power of non-adjustable clean energy in period n and t; It indicates that the amount of water power abandoned by adjustable clean energy n; It represents the power output of conventional power source in period c and t; It represents the power generation output of clean energy in period n and t; Indicates the power load during time period t; Indicates the maximum technical output of conventional power source c; It indicates the minimum output corresponding to the maximum peak-shaving capacity provided by conventional power source c; It represents the predicted power generation output of non-adjustable clean energy in period n and t; represents the maximum technical output of adjustable clean energy n; represents the minimum technical output of adjustable clean energy n; represents the maximum power generation of adjustable clean energy n; Represents the minimum power generation of the adjustable clean energy n.

[0080] It should be noted that by solving the multi-period optimization simulation model, the conventional power generation output plan under the maximum clean energy consumption can be obtained. This model is essentially a linear programming problem and can be solved using the simplex method or by calling commercial software packages such as Cplex.

[0081] The peak load regulation capacity of conventional power sources on a typical operating day is the peak load regulation power corresponding to the output of conventional power sources on a typical operating day being lower than the minimum technical output, expressed as:

[0082]

[0083] Among them, E A,PR Indicates the peak load regulation capability of conventional power supply in a typical operating day; It represents the minimum technical output of conventional power source c, that is, the minimum power generation output without providing peak load regulation service.

[0084] Furthermore, based on the peak-shaving demand on each operating day in the forecast period and the peak-shaving capacity in the forecast period, the peak-shaving situation is predicted, the amount of abandoned wind, solar and hydropower is evaluated, and an early warning on clean energy consumption is issued.

[0085] The expected abandoned wind, solar and hydropower electricity on any operating day in the forecast period is the difference between the peak-shaving demand and the peak-shaving capacity, expressed as:

[0086]

[0087] in, It represents the expected abandoned wind, solar and hydropower electricity on the operating day d in the forecast interval.

[0088] Clean energy consumption warning is carried out based on the abandoned wind, solar and hydropower volumes on the forecasted days. The specific standards are as follows:

[0089] (1) If the number of days on which the expected wind, solar, and hydropower curtailment occurs exceeds the limit, a red alert will be issued, indicating that it is difficult to absorb the overall clean energy within the forecast period and there is a large amount of wind, solar, and hydropower curtailment. The judgment criteria can be expressed as:

[0090] NWPD ≥ NWPD set

[0091] Among them, NWPD represents the number of days in which wind, solar and hydropower are abandoned within the forecast period; NWPD set It represents the red warning limit for wind, solar and hydropower abandonment within the forecast interval. Generally, the number of days corresponding to the limit is required to be 80% of the total days in the forecast interval.

[0092] (2) If there are expected days of wind, solar and hydropower abandonment, but the total number of days does not exceed the limit, an orange warning will be issued, which means that the overall clean energy consumption is difficult within the forecast period, and some operating days will have wind, solar and hydropower abandonment. The judgment standard can be expressed as:

[0093] 0 <NWPD<NWPD set

[0094] (3) If the expected wind, solar and hydropower abandonment on each operating day in the forecast period is 0, but there is a peak load demand on each operating day, a yellow warning is issued, that is, there is no wind, solar and hydropower abandonment, but the peak load pressure is large. The judgment standard can be expressed as:

[0095]

[0096] The present embodiment also provides a power grid clean energy consumption early warning system based on multi-source indicators, characterized in that it includes: a data acquisition module, which collects historical power grid operation data through a power grid energy management system, and collects forecast data of the operation day to be evaluated through a power generation plan management system; a function construction module, which analyzes the intrinsic relationship between power supply sufficiency and peak load regulation, wind power abandonment, solar power abandonment and hydropower abandonment, counts the peak load regulation power of conventional power sources and the wind power abandonment, solar power abandonment and hydropower abandonment on each operation day, and fits and generates a peak load regulation demand-power supply sufficiency function to evaluate the peak load regulation demand in the prediction interval; an early warning module, which determines a typical operation day within the prediction interval, and constructs a multi-period optimization simulation model to evaluate the peak load regulation capacity, and issues a clean energy consumption early warning according to the difference between the peak load regulation demand and the peak load regulation capacity.

[0097] This embodiment also provides a computing device, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the power grid clean energy consumption early warning method based on multi-source indicators as proposed in the above embodiment.

[0098] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for early warning of clean energy consumption in a power grid based on multi-source indicators as proposed in the above embodiment is implemented.

[0099] The storage medium proposed in this embodiment and the power grid clean energy consumption early warning method based on multi-source indicators proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0100] Through the above description of the implementation method, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccessMemory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0101] Example 2

[0102] The following is an embodiment of the present invention, which provides a power grid clean energy consumption early warning method based on multi-source indicators. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0103] In order to evaluate the power surplus and peak-shaving capacity in power grid operation and maximize the consumption of clean energy, a simulation experiment was conducted. The experiment was divided into five steps: historical data collection, data analysis, fitting model construction, peak-shaving demand prediction and early warning evaluation.

[0104] Taking the simulation data of a typical operation day as an example, the power abundance index is calculated by the method of the present invention, which is expressed as:

[0105]

[0106] Forecast electricity consumption Set it to 50000MWh, the network loss rate η is 0.05, the predicted power generation of clean energy n is 15000MWh, and the minimum technical output is 20000MWh. Substituting it into the calculation, we get:

[0107]

[0108] The sum of peak load, abandoned wind, solar and hydropower is the peak load demand required to meet the clean energy consumption of the power grid. The peak load demand is calculated as follows:

[0109]

[0110] For the convenience of calculation, assume that a PR is 5000, b PR is 1000, then the peak demand The peak load regulation capacity of conventional power sources is calculated using a multi-period optimization simulation model, which is about 10,000 MWh. The expected wind, solar, and hydropower abandonment is expressed as:

[0111]

[0112] At this time, the expected amount of wind, solar and hydropower abandoned is 375MWh, indicating that the peak-shaving demand exceeds the peak-shaving capacity, and there is a waste of clean energy on this day.

[0113] Calculate the expected wind, solar and hydropower curtailment on each operating day in the forecast interval, and set the red warning limit to 80% of the total days in the forecast interval. When the total days in the forecast interval are ten days, the red warning limit is eight days, that is, if the number of days for wind, solar and hydropower curtailment in the forecast interval is greater than or equal to eight days, a red warning will be issued.

[0114] Compared with traditional clean energy consumption early warning methods, traditional clean energy consumption early warning methods are essentially to evaluate the peak-shaving situation at the moment when the peak-shaving pressure is the greatest on a single operating day, which serves as the basis for issuing clean energy consumption early warnings. However, clean energy consumption is an analysis of the amount of abandoned wind, solar and hydro power. The abandoned wind, solar and hydro power situation in a single period cannot reflect the overall clean energy consumption situation.

[0115] The method of the present invention constructs an expected peak-shaving demand assessment method based on power abundance, and calculates the expected peak-shaving capacity within the expected interval through typical operation day analysis. By comparing the peak-shaving demand and peak-shaving capacity, the wind, solar and hydropower abandonment on each operation day in the forecast interval is evaluated and calculated. The wind, solar and hydropower abandonment on each operation day in the forecast interval is comprehensively considered, and a clean energy consumption warning is issued.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for early warning of clean energy consumption in power grid based on multi-source indicators, characterized in that: include: Collect historical grid operation data and forecast data for the operation day to be evaluated; Analyze the intrinsic relationship between power supply surplus and peak load regulation, wind power abandonment, solar power abandonment and hydropower abandonment, construct a fitting function, and evaluate the peak load regulation demand in the forecast interval; Select typical operating days, evaluate peak-shaving capacity, and issue clean energy consumption warnings based on the difference between peak-shaving demand and peak-shaving capacity.

2. The method for early warning of clean energy consumption in a power grid based on multiple source indicators according to claim 1, characterized in that: The power grid historical operation data includes historical daily power load, clean energy power generation forecast, upper and lower limits of conventional power generation output, and abandoned wind, solar and hydropower generation, which are derived from the power grid energy management system; The forecast data for the operating day to be evaluated includes electricity load, clean energy power generation forecast, upper and lower limits of conventional power generation output and peak-shaving capacity, which are derived from the power generation planning management system.

3. The method for early warning of clean energy consumption in power grid based on multi-source indicators according to claim 2, characterized in that: The analysis of the intrinsic relationship between the power surplus and peak load regulation, wind power abandonment, solar power abandonment and hydropower abandonment includes that the power surplus index of any operating day is defined as the ratio of the conventional power generation demand determined by the predicted power consumption and clean energy power generation demand to the minimum power generation corresponding to its minimum technical output, expressed as: in, Indicates the power abundance index on the operating day; It represents the demand for conventional power generation determined by the predicted power consumption and clean energy power generation demand; Indicates the minimum power generation corresponding to the minimum technical output of conventional power sources; Conventional power generation demand determined by predicted power consumption and clean energy generation demand It is equal to the power generation demand calculated based on the predicted power consumption minus the predicted clean energy power generation, expressed as: in, represents the predicted power consumption on the operation day d; η represents the network loss rate; represents the predicted power generation of clean energy source n on operating day d; NN represents the number of clean energy power stations.

4. The method for early warning of clean energy consumption in a power grid based on multiple source indicators according to claim 3, characterized in that: The fitting function includes counting the peak load of conventional power sources and the abandoned wind, solar and hydropower on each operating day, and fitting to generate a peak load demand-power margin function, which is expressed as: In the formula, represents the peak-shaving demand of the operating day of the fitted production; a PR represents the coefficient of the linear term in the linear function fitting function; b PR Represents the constant term coefficient in the linear function fitting function; λ Emax It represents the maximum value of the power surplus corresponding to the operation day with peak load regulation and abandoned wind, solar and hydropower calculated from historical data; Emin It indicates the minimum value of electricity surplus corresponding to the operating day with peak-shaving electricity and abandoned wind, solar and hydroelectric power, calculated based on historical data.

5. The method for early warning of clean energy consumption in a power grid based on multiple source indicators according to claim 4, characterized in that: The evaluation of the peak load demand in the forecast interval includes taking the clean energy power and power consumption forecast accuracy as the judgment criteria for the forecast interval, which is expressed as: In the formula, ε N,set Represents the clean energy power forecast accuracy limit; ε P,set Indicates the accuracy limit of electricity consumption prediction; Indicates the prediction accuracy of clean energy power on the operating day; It indicates the prediction accuracy of clean energy electricity on the operation day d+1; Indicates the prediction accuracy of power consumption on operation day d; It indicates the prediction accuracy of electricity consumption on the operating day d+1. It calculates the electricity surplus of each operating day within the prediction interval, and predicts the peak load demand of each operating day within the prediction interval through the fitting function.

6. The method for early warning of clean energy consumption in a power grid based on multiple source indicators according to claim 5, characterized in that: The selection of a typical operation day and the evaluation of the peak load-shaving capacity include determining a typical operation day within a prediction interval, and constructing a multi-period optimization simulation model to evaluate the peak load-shaving capacity that the conventional power source can provide on the typical operation day. The multi-period optimization simulation model is expressed as: Among them, NonGN represents the non-adjustable clean energy set; GN represents the adjustable clean energy set; It represents the abandoned wind and solar power of non-adjustable clean energy in period n and t; It indicates the amount of water power that can be abandoned by regulating clean energy; It represents the power output of conventional power source in period c and t; It represents the power generation output of clean energy in period n and t; Indicates the power load during time period t; Indicates the maximum technical output of conventional power source c; It indicates the minimum output corresponding to the maximum peak-shaving capacity provided by conventional power source c; It represents the predicted power generation output of non-adjustable clean energy in period n and t; represents the maximum technical output of adjustable clean energy n; represents the minimum technical output of adjustable clean energy n; represents the maximum power generation of adjustable clean energy n; represents the minimum power generation of the adjustable clean energy n; NT represents the number of time periods; ΔT represents the time interval; NC represents the number of conventional power sources; The peak load regulation capacity of conventional power sources on a typical operating day is the peak load regulation power corresponding to the output of conventional power sources on a typical operating day being lower than the minimum technical output, expressed as: Among them, E A,PR Indicates the peak load regulation capability of conventional power supply in a typical operating day; Indicates the minimum technical output of conventional power supply c.

7. The method for early warning of clean energy consumption in a power grid based on multiple source indicators according to claim 6, characterized in that: The clean energy consumption warning includes predicting the peak-shaving situation, evaluating the abandoned wind, solar and hydroelectric power, and issuing a clean energy consumption warning based on the peak-shaving demand of each operating day in the forecast interval and the peak-shaving capacity of the forecast interval. The expected abandoned wind, solar and hydroelectric power on any operating day in the forecast interval is the difference between the peak-shaving demand and the peak-shaving capacity, expressed as: in, It represents the expected abandoned wind, solar and hydropower electricity on the operation day d in the forecast interval; If the number of days on which the wind, solar and hydropower curtailment is expected to occur exceeds the limit, a red alert will be issued; If there are days when wind, solar and hydropower are expected to be abandoned, but the total number of days does not exceed the limit, an orange warning will be issued; If the expected wind, solar and hydropower abandonment on each operating day in the forecast period is 0, but there is peak-shaving demand on each operating day, a yellow warning will be issued.

8. A power grid clean energy consumption early warning system based on multi-source indicators using the method according to any one of claims 1 to 7, characterized in that: include, The data collection module collects historical grid operation data through the grid energy management system and collects forecast data of the operation day to be evaluated through the power generation plan management system; Function construction module, analyze the intrinsic relationship between power supply sufficiency and peak load regulation, wind power curtailment, solar power curtailment and hydropower curtailment, count the peak load regulation power of conventional power sources and the curtailment power of wind power curtailment, solar power curtailment and hydropower curtailment on each operating day, and fit and generate the peak load regulation demand-power supply sufficiency function to evaluate the peak load regulation demand in the forecast interval; The early warning module determines the typical operating day within the forecast range, builds a multi-period optimization simulation model, evaluates the peak-shaving capacity, and issues clean energy consumption early warnings based on the difference between peak-shaving demand and peak-shaving capacity.

9. A computing device comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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