New energy consumption measuring and calculating method and device giving consideration to double increase of electric power and electric quantity

Abstract

The invention provides a new energy consumption measuring and calculating method and device considering double increase of electric power and electric quantity, and relates to the technical field of electric power grids. According to the method, on the basis of power grid power supply information, historical operation data and prediction plan data, multivariate data such as a conventional power supply operation mode, a new energy power generation sequence, a load sequence and a tie line sequence of a target regional power grid are constructed, so that a new energy consumption measurement and calculation model considering double increase of electric power and electric quantity is established; and a mixed integer linear programming algorithm is adopted to obtain an optimal solution, so that the new energy utilization rate of the target regional power grid is calculated, and new energy consumption calculation is realized. According to the method, the new energy consumption calculation is comprehensively carried out by considering multiple factors, so that the new energy consumption calculation result conforms to the actual condition of the power grid, and the accuracy of the new energy consumption calculation result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power grids, and in particular to a new energy consumption calculation method and device that takes into account both the growth of electric power and electricity. Background Art

[0002] High-penetration renewable energy generation brings clean and green electricity, but its strong volatility and uncertainty lead to increasing pressure on the balance of power and electricity in the power grid, and increasingly severe problems in ensuring power consumption and supply. How to achieve efficient use of renewable energy while ensuring safe and stable operation of the power grid, estimate the power grid's power consumption capacity in advance, and formulate response measures have become key links in the construction of new power systems. Therefore, the measurement and evaluation of new energy consumption capacity is crucial.

[0003] The essence of new energy consumption calculation is an optimization problem with the goal of maximizing new energy power generation under the energy balance of the power grid. The current new energy consumption calculation method only considers how to adjust the power supply of conventional thermal power units, or only considers historical operating data for power generation or load sequences, which cannot fully reflect the actual operating conditions, resulting in incomplete new energy consumption calculation, calculation results that do not conform to the actual situation of the power grid, and inaccurate calculation results. Summary of the invention

[0004] The present invention provides a method and device for calculating new energy consumption that takes into account the dual growth of electric power and electricity. It can comprehensively calculate the new energy consumption from multiple factors such as electric power and electricity, so that the new energy consumption calculation results are in line with the actual situation of the power grid, thereby improving the accuracy of the new energy consumption calculation results.

[0005] In the first aspect, the present invention provides a new energy consumption estimation method that takes into account the dual growth of electric power and electricity, the method comprising: obtaining multivariate data of the target area power grid, the multivariate data comprising power grid power supply information, historical operation data and forecast plan data; based on the multivariate data, establishing power generation and load sequences, and constructing a new energy consumption estimation model; the new energy consumption estimation model takes the maximum new energy power generation as the objective function, and takes power balance constraints and equipment attribute constraints as constraints; based on the new energy consumption estimation model, a mixed integer linear programming algorithm is used to solve and obtain the optimal solution; the optimal solution comprises the power generation of each type of new energy in each unit time period within the measurement period; based on the optimal solution, the new energy utilization rate of the target area power grid is calculated to realize the new energy consumption estimation.

[0006] In the second aspect, an embodiment of the present invention provides a new energy consumption estimation device that takes into account the dual growth of electric power and electricity, and the device includes: a communication module, which is used to obtain multivariate data of the target area power grid, and the multivariate data includes power grid power supply information, historical operation data and forecast plan data; a processing module, which is used to establish power generation and load sequences based on the multivariate data, and construct a new energy consumption estimation model; the new energy consumption estimation model takes the maximum new energy power generation as the objective function, and takes power balance constraints and equipment attribute constraints as constraints; based on the new energy consumption estimation model, a mixed integer linear programming algorithm is used to solve and obtain the optimal solution; the optimal solution includes the power generation of each type of new energy in each unit time period within the measurement period; based on the optimal solution, the new energy utilization rate of the target area power grid is calculated to realize new energy consumption estimation.

[0007] In a third aspect, an embodiment of the present invention provides a terminal device, comprising a memory and a processor, the memory storing a computer program, the processor being used to call and run the computer program stored in the memory to perform the steps of the method described in the first aspect and any possible implementation method of the first aspect.

[0008] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein when the computer program is executed by a processor, the steps of the method described in the first aspect and any possible implementation method of the first aspect are implemented.

[0009] The present invention provides a new energy consumption calculation method and device that takes into account the dual growth of electric power and electricity. The present invention is based on power grid power information, historical operation data and forecast plan data, and constructs multivariate data such as conventional power supply operation mode, new energy power generation sequence, load sequence, and tie line sequence of the target area power grid to establish a new energy consumption calculation model that takes into account the dual growth of electric power and electricity, and adopts a mixed integer linear programming algorithm to obtain the optimal solution, and then calculate the new energy utilization rate of the target area power grid to achieve new energy consumption calculation. The present invention considers multiple factors to comprehensively and comprehensively calculate new energy consumption, so that the new energy consumption calculation result conforms to the actual situation of the power grid and improves the accuracy of the new energy consumption calculation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0011] Figure 1 It is a flow chart of a new energy consumption calculation method taking into account the double growth of electric power and electricity provided by an embodiment of the present invention;

[0012] Figure 2 This is a schematic diagram of calculating the theoretical output of a monthly centralized photovoltaic system provided by an embodiment of the present invention;

[0013] Figure 3 This is a schematic diagram of calculating monthly wind power theoretical output provided by an embodiment of the present invention;

[0014] Figure 4 It is a schematic diagram of a full-caliber load sequence iteration process curve provided by an embodiment of the present invention;

[0015] Figure 5 It is a schematic diagram of calculating a monthly full-caliber load curve provided by an embodiment of the present invention;

[0016] Figure 6 It is a schematic diagram of a curve for calculating monthly renewable energy abandonment provided by an embodiment of the present invention;

[0017] Figure 7 This is a schematic diagram of a curve for calculating monthly thermal power generation capacity provided by an embodiment of the present invention;

[0018] Figure 8 This is a schematic diagram of a monthly thermal power output curve provided by an embodiment of the present invention;

[0019] Fig. 9 It is a structural schematic diagram of a new energy consumption measurement and calculation device provided by an embodiment of the present invention;

[0020] Fig.10 It is a structural diagram of a terminal device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0022] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0023] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include other steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products or devices.

[0024] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following will be described through specific embodiments in conjunction with the accompanying drawings of the present invention.

[0025] As described in the background technology, the current new energy consumption calculation method considers only a single factor, resulting in incomplete new energy consumption calculation, the calculation results do not conform to the actual situation of the power grid, and the calculation results are inaccurate.

[0026] To solve this technical problem, the present invention provides a new energy consumption calculation method and terminal equipment based on power grid information, historical operation data, and forecast plan data, taking into account the influence of multiple factors such as conventional power supply operation mode, new energy power generation sequence, load sequence, and tie line sequence, taking into account the dual growth of power and electricity, and being more practical. The established model is a mixed integer linear programming model, which enables convenient establishment and rapid optimization of the power grid model. In addition, for unsolvable data, the power of the tie line can be adjusted to achieve balance by analyzing the startup mode of conventional power supply in adjacent weeks, solving the modeling and solution problems faced by the new energy consumption calculation.

[0027] like Figure 1 As shown, the embodiment of the present invention provides a new energy consumption estimation method taking into account the dual growth of electric power and electricity. The method includes steps S101-S104.

[0028] S101, obtaining multi-dimensional data of the power grid in the target area.

[0029] In the embodiment of the present application, the multivariate data includes grid power information, historical operation data and forecast plan data.

[0030] In some embodiments, the grid power supply information includes grid information and power supply information; the grid information includes peak, flat and valley time period information of each interconnection line and regional section information; the power supply information includes equipment attribute information of conventional thermal power units, equipment attribute information of electrochemical energy storage, and equipment attribute information of pumped storage power stations.

[0031] For example, the model, capacity, ramp rate, etc. of conventional thermal power units, the power, capacity, conversion efficiency, etc. of electrochemical energy storage, the maximum and minimum water storage capacity, unit capacity, conversion efficiency, etc. of pumped-storage power stations, as well as basic information on other possible conventional power sources such as hydropower and biomass power generation.

[0032] In some embodiments, the historical operation data includes the full-caliber load power sequence of the previous measurement year, the theoretical data of wind power in the previous N years, and the theoretical data of photovoltaic power in the previous N years. For example, the theoretical output sequence of wind power and photovoltaic power in the previous three years, resource hours, etc.

[0033] In some embodiments, the forecast planning data includes daily electricity forecast data for the current calculation year, monthly electricity forecast data, monthly wind power installed capacity, monthly photovoltaic installed capacity, wind power forecast resource hours, photovoltaic forecast resource hours and monthly interconnection line plan sequence.

[0034] S102. Based on multivariate data, establish power generation and load sequences, and build a new energy consumption estimation model.

[0035] In the embodiment of the present application, the new energy consumption calculation model takes the maximum new energy power generation as the objective function, and takes the power balance constraint and the equipment property constraint as the constraint conditions.

[0036] As a possible implementation manner, step S102 may be specifically implemented as steps S1021 - S1025 .

[0037] S1021. Determine the planning sequence of the interconnection line in each time period based on the power grid information.

[0038] Exemplarily, the tie line planning sequence for each period, such as the tie line 8760-point planning sequence, can be calculated by the following formula, which is composed of the sum of multiple tie lines in the target power grid area:

[0039]

[0040] n∈Γtran t∈[1,8760]

[0041] Where Ptran(t) is the sum of the power of the target grid area tie lines at time t, is the planned output of the nth tie line at time t, αn(t) is the peak-flat-valley time-sharing coefficient of the nth tie line at time t, and Γtran is the set of all tie lines in the target power grid area.

[0042] S1022. Based on the power supply information, determine the planning sequence for each time period of the conventional thermal power units.

[0043] For example, the planning sequence of each period of conventional thermal power units, such as the 8760-point planning sequence of thermal power units, needs to be based on the forecast planning data such as the minimum operation mode of the thermal power units, distinguish between the heating period and the non-heating period to obtain the maximum number of m-type thermal power units at time t. Minimum number of units to be started and the ratio of the maximum operating mode to the unit capacity The proportion of the minimum operating mode to the unit capacity The specific operating power of the thermal power unit will be subject to these two constraints and can be expressed as:

[0044]

[0045] in, is the actual operating power of the thermal power unit at time t, is the capacity of the m-type thermal power unit, is the ratio of the number of thermal power units in operation to the unit capacity at time t, It is the proportion of the thermal power unit operating mode at time t to the unit capacity.

[0046] S1023. Based on the historical operation data and forecast plan data, perform power generation forecasting to determine the photovoltaic power output sequence in each time period and the wind power output sequence in each time period.

[0047] Exemplarily, step S1023 may be specifically implemented as steps A1-A4.

[0048] A1. Based on the theoretical data of photovoltaic power generation in the previous N years and the monthly installed capacity of photovoltaic power generation, the average value of photovoltaic resource sequence for each month is obtained.

[0049] For example, the photovoltaic output sequence in each period, the photovoltaic 8760-point resource sequence, is calculated in the following way, including sequence trend calculation and resource hour adjustment.

[0050] The first step is trend calculation, which measures the annual photovoltaic output trend, which is the average value of the resource sequence obtained by dividing the photovoltaic theoretical power generation in the previous three years by the photovoltaic installed capacity in that month. The specific formula is as follows.

[0051]

[0052]

[0053] in, is the average value of the resource sequence at time t in the current measurement year, The theoretical photovoltaic output at time t in the previous measurement year, It is the photovoltaic installed capacity at time t in the previous measurement year, where the installed capacity is counted on a monthly basis. is the number of PV resource hours in the current measurement month of the current measurement year, obtained by summing up all resource sequences in that month, and N-day is the number of days in that month.

[0054] A2. Based on the average value of photovoltaic resource sequence in each month and the predicted photovoltaic resource hours, determine the photovoltaic output sequence in each period.

[0055] For example, the embodiment of the present invention can also be adjusted based on resource hours, in units of months, according to the predicted resource hours. Adjust the resource sequence as shown in the following formula.

[0056]

[0057] After obtaining the 8760-point resource sequence for the whole year, multiplying it by the photovoltaic installed capacity of each month month by month, we can get the photovoltaic output sequence for each period of the current measured year. The photovoltaic 8760-point theoretical output sequence P pv (t).

[0058] P pv (t) = R pv (t)×CAP pv (t)t∈[1,N-day];

[0059] Among them, R pv (t) is the resource sequence of 8760 points throughout the year, is the average value of the resource sequence at time t in the current measurement year, is the predicted photovoltaic resource hours for the current measurement month of the current measurement year, is the number of photovoltaic resource hours in the current measurement month of the current measurement year, N-day is the number of days in the current measurement month, P pv (t) is the photovoltaic output sequence in each period, CAP pv (t) is the photovoltaic installed capacity of each month.

[0060] A3. Based on the theoretical data of wind power in the previous N years and the monthly installed capacity of wind power, the average value of the wind power resource sequence for each month is obtained.

[0061] For example, the wind power output sequence in each period, the wind power 8760-point resource sequence, is calculated in the following way, including sequence trend calculation and resource hour adjustment.

[0062] The first step is trend calculation, which measures the annual wind power output trend, which is the average value of the resource sequence obtained by dividing the theoretical wind power generation in the previous three years by the wind power installed capacity in that month. The specific formula is as follows.

[0063]

[0064] in, is the average value of the resource sequence at time t in the current measurement year, is the theoretical wind power output at time t in the previous measurement year, It is the wind power installed capacity at time t in the previous measurement year, where the installed capacity is counted on a monthly basis. It is the number of wind power resource hours in the current measurement month of the current measurement year, which is obtained by summing up all resource sequences in that month. N-day is the number of days in that month.

[0065] A4. Based on the average value of wind power resource sequence in each month and the predicted wind power resource hours, determine the wind power output sequence in each period.

[0066] For example, the embodiment of the present invention can also be adjusted based on resource hours, in units of months, according to the predicted resource hours. Adjust the resource sequence as shown in the following formula.

[0067]

[0068] After obtaining the 8760-point resource sequence for the whole year, multiplying it by the wind power installed capacity of each month month by month, we can get the wind power output sequence for each period of the current estimated year. The wind power 8760-point theoretical output sequence P wind (t).

[0069] P wind (t) = R wind (t)×CAP wind (t)t∈[1,N-day];

[0070] Among them, R wind (t) is the resource sequence of 8760 points throughout the year, is the average value of the resource sequence at time t in the current measurement year, is the predicted wind power resource hours for the current measurement month of the current measurement year, is the number of wind power resource hours in the current measurement year and the current measurement month, N-day is the number of days in the current measurement month, P wind (t) is the wind power output sequence in each period, CAP wind (t) is the installed capacity of wind power in each month.

[0071] S1024. Determine the full-caliber load sequence for each time period based on historical operation data and forecast plan data.

[0072] It should be noted that the load sequence construction needs to obtain a daily normalized sequence through the full-caliber sequence of the historical load of the previous measurement year, and then calculate it month by month in an iterative manner, taking into account the dual growth of electricity and power, to obtain the full-caliber load sequence of the measurement year. The traditional method only obtains the daily normalized sequence and multiplies it by the maximum value of the daily load forecast to obtain the full-caliber load sequence, but the power is actually the factor that has the greatest impact on the consumption, or it is only converted according to the power ratio, but it cannot accurately reflect the impact of power growth. The present invention takes into account that according to the scheduling plan, the daily full-caliber maximum load and the monthly full-caliber load power can be predicted, so the electricity is converted in days and the power is converted in months.

[0073] Exemplarily, step S1024 may be specifically implemented as steps one to five.

[0074] Step 1: Based on the full-caliber load power sequence of the previous measurement year, normalization is performed to obtain a daily normalized load sequence.

[0075]

[0076] in, is the daily normalized sequence of load, that is, the normalized sequence of a certain day in the previous measurement year, is the full-caliber load sequence on a certain day in the previous measurement year, It is the maximum value of the full-caliber load sequence on a certain day in the previous measurement year.

[0077] Step 2: Based on the daily normalized load sequence and the daily electricity forecast data of the current measurement year, the electricity conversion of the daily full-caliber load is performed to obtain the electricity conversion sequence.

[0078] For example, the embodiment of the present invention can calculate the daily full-caliber load according to the predicted power based on the normalized sequence, which can be called calculating the power conversion sequence. The embodiment of the present invention can determine the power conversion sequence based on the following formula.

[0079]

[0080] Among them, P load-p (t) is the electricity conversion sequence, is the daily normalized load series, It is the maximum daily forecast load of all scopes in the daily electricity forecast data of the current calculation year.

[0081] Step three, based on the electricity conversion sequence and the monthly electricity forecast data, perform monthly electricity conversion to obtain the electricity conversion sequence.

[0082] For example, the power conversion has been completed, and the maximum value of the full-caliber load meets the prediction condition on a daily basis. Next, on this basis, the power conversion is performed on a monthly basis, which can be called calculating the power conversion sequence. The embodiment of the present invention can determine the power conversion sequence based on the following formula.

[0083]

[0084] Among them, P load-e (t) is the electricity conversion sequence, P load-p (t) is the electricity conversion sequence, It is the monthly electricity consumption forecast data, and N-day is the number of days in the measurement month.

[0085] Step 4: Based on the electricity conversion sequence and the daily full-caliber maximum predicted load, calculate the deviation between the daily maximum load and the maximum predicted load.

[0086] Step 5: If the deviation is less than or equal to the set threshold, the electricity conversion sequence in the current iteration process is determined as the full-caliber load sequence for each period, and the iteration process is exited. If the deviation is greater than the set threshold, steps 1 to 5 are repeated until the iteration process is exited.

[0087] It should be noted that the electricity conversion is achieved by calculating the difference between the target electricity and the current electricity, and then dividing it into hourly values. However, the converted daily maximum load deviates from the maximum predicted load. Therefore, based on the current sequence, the above process is repeated to calculate the daily normalized sequence, the electricity conversion sequence, and the electricity conversion sequence. In this way, the daily maximum load will remain consistent with the maximum predicted load, and the electricity difference will become smaller and smaller until the required conditions are met, and a full-caliber load sequence that takes into account the dual growth of electricity and electricity is obtained.

[0088] S1025. Construct a new energy consumption calculation model based on the planning sequence of the interconnection line in each time period, the planning sequence of conventional thermal power units in each time period, the output sequence of photovoltaic power in each time period, the output sequence of wind power in each time period, and the full-caliber load sequence in each time period.

[0089] Exemplarily, step S1025 may be specifically implemented as steps B1-B4.

[0090] B1. Construct the objective function based on the photovoltaic power output sequence in each period and the wind power output sequence in each period.

[0091] For example, under the energy balance constraint, the objective function and constraint conditions are constructed with the goal of maximizing the actual power generation of new energy, where the objective function can be expressed as:

[0092]

[0093] Among them, f(t) is the objective function, T is the step length of the measurement period, and T=8760 for the whole year. Of course, it is also possible to measure the consumption of only a certain month. is the concentrated photovoltaic power generation at time t, is the wind power generation at time t.

[0094] B2. Construct power balance constraints based on the planning sequence of the interconnection line in each period, the planning sequence of conventional thermal power units in each period, the output sequence of photovoltaic power in each period, the output sequence of wind power in each period and the full-caliber load sequence in each period.

[0095] Exemplarily, the power balance constraint may be expressed as the following formula.

[0096]

[0097] in, is the concentrated photovoltaic power generation at time t, is the amount of centralized photovoltaic power abandonment at time t, P cpv (t) is the theoretical power of centralized photovoltaic at time t, is the wind power generation at time t, is the amount of wind power abandoned at time t, P wind (t) is the theoretical wind power at time t, P dpv (t) is the distributed photovoltaic power generation at time t. Distributed photovoltaic power generation is temporarily considered to be fully consumed without abandoned power. coal (t) is the sum of the power generation of all conventional thermal power units at time t, Γ coal is the set of thermal power unit types. load (t) is the full-diameter load at time t, P other (t) is the power generation of other types of non-adjustable power sources at time t, P bat (t) is the charge and discharge amount of electrochemical energy storage at time t, P tran (t) is the tie line power. is the power generation of the mth conventional thermal power unit at time t.

[0098] B3. Based on the power supply information, equipment attribute constraints are constructed. The equipment attribute constraints include the upper and lower limit constraints and ramp rate constraints of conventional thermal power units, system positive and negative standby constraints, new energy operation constraints and energy storage operation constraints.

[0099] Exemplarily, the upper and lower operating limit constraints of a conventional thermal power unit can be expressed as the following formula.

[0100]

[0101] in, is the power generation of type m thermal power unit at time t. is the installed capacity of the M-type thermal power unit, is the number of m-type thermal power units in operation at time t, is the operation mode of the M-type thermal power unit. is the minimum number of m-type thermal power units in operation at time t, is the maximum number of m-type thermal power units in operation at time t, is the proportion of the minimum operating mode to the unit capacity, is the proportion of the maximum operating mode to the unit capacity, Γ coal A collection of thermal power unit types.

[0102] For example, If it is 0.5, it means that the M-type thermal power unit is operating at 50% output.

[0103] Exemplarily, the ramp rate constraint of a conventional thermal power unit and the system positive and negative reserve constraints can be expressed as the following formula.

[0104]

[0105]

[0106] t∈[1,8760]m∈Γ coal

[0107] in, is the power generation of type m thermal power unit at time t+1, is the power generation of type m thermal power unit at time t, is the ramp rate of conventional thermal power units, is the ramp down rate of conventional thermal power units. is the system's positive spare capacity. It is the negative spare capacity of the system.

[0108] It should be noted that is the ramp up rate and ramp down rate of the m-type thermal power unit, is the system's positive and negative spare capacity. It can be seen that at this time, the output of the m-type thermal power unit is the product of the number of units in operation in the system and the proportional coefficient. The multiplication of the two variables will cause the established model to be a nonlinear model, which increases the difficulty of solving. The present invention makes improvements on this basis. Instead of optimizing the proportional coefficient, the output of the thermal power unit is set as a separate variable, and the number of units in operation is put into the upper and lower limit constraints to produce an effect, so that the model is a mixed integer linear optimization model, which greatly reduces the difficulty of solving. The present invention modifies the upper and lower limit constraints of the above-mentioned thermal power unit operation to:

[0109]

[0110] t∈[1,8760]m∈Γ coal

[0111] And considering that the conventional power supply startup mode is that the minimum start-up and shutdown cycle of the unit is once a week, the number of started units needs to be optimized week by week, and the number of started units within each week must remain consistent.

[0112] Exemplarily, the new energy operation constraints and the energy storage operation constraints can be expressed as the following formulas.

[0113]

[0114]

[0115]

[0116] Among them, E bat (t+1) is the energy stored at time t, CAP bat (t) is the installed capacity of energy storage, η bat For the efficiency of energy storage.

[0117] B4. Based on the objective function, power balance constraints and equipment attribute constraints, a new energy consumption estimation model is constructed.

[0118] S103. Based on the new energy consumption calculation model, a mixed integer linear programming algorithm is used to solve and obtain the optimal solution.

[0119] In the embodiment of the present application, the optimal solution includes calculating the power generation of various types of renewable energy in each unit time period within the time period.

[0120] The embodiment of the present invention can solve the mixed integer linear optimization model established above by calling the CPLEX algorithm with the help of the YALMIP solver in MATLAB.

[0121] As a possible implementation manner, step S103 may be specifically implemented as steps S1031 - S1034 .

[0122] S1031. Use a mixed integer linear programming algorithm to solve the new energy consumption calculation model and obtain the model output result.

[0123] S1032. If the model output result indicates that there is a solution, the solution in the model output result is determined as the optimal solution.

[0124] S1033. If the model output result is that there is no solution, determine the time period when there is no solution, and determine the reason for the no solution based on the power generation data and load data of the time period when there is no solution.

[0125] In some embodiments, the unsolvable reasons include too much load and insufficient power generation, or too little load and excessive power generation.

[0126] S1034. Based on the reasons for the unsolvable problem and the power generation conditions in the adjacent time periods of the unsolvable time period, recalculate and modify the plan sequence of each time period of the tie line to obtain an updated plan sequence of each time period of the tie line.

[0127] Exemplarily, if the reason for no solution is excessive load and insufficient power generation, then the maximum output data of the adjacent time periods of the no-solution period and the new energy consumption calculation model are used to recalculate and obtain recalculated results. Based on the recalculated results and with the power balance constraint as the goal, the plan sequence for each time period of the interconnection line is modified to obtain an updated plan sequence for each time period of the interconnection line.

[0128] Another example is that if the reason for no solution is that the load is too small and the power generation is too large, then the minimum output data of the adjacent time periods of the no-solution period and the new energy consumption calculation model are used to recalculate and obtain the recalculated results. Based on the recalculated results and with the power balance constraint as the goal, the plan sequence of each time period of the interconnection line is modified to obtain an updated plan sequence of each time period of the interconnection line.

[0129] It should be noted that the specific expression of the unsolvable data optimization processing is that, due to the limitation on the number of thermal power units in operation, there may be a situation in which all thermal power units are fully operated within a week, there is no abandoned electricity from new energy but the load demand is still not met, or the load is too small and the minimum operating mode of the thermal power units is still greater than the load demand. At this time, the calculation will have no solution.

[0130] Considering that the thermal power units are started and stopped once a week, and the startup capacity of the thermal power units between adjacent weeks is limited by the ramp rate constraint and is related to the previous week, this patent proposes a method of bringing the startup mode of the previous week into the unsolvable weekly power calculation deviation, and then processing it by modifying the tie line to make up the error. It mainly includes two steps:

[0131] First, we calculate and determine whether the problem is that the load is too heavy, resulting in insufficient power generation, or that the load is too light, resulting in excessive power generation.

[0132] Afterwards, if there is no solution due to excessive load, the maximum output of the previous week will be Bring the model in for calculation, calculate the deviation for the moment of insufficient power, and deal with it by modifying the tie line. The model is brought into calculation, and when the power is too large, the deviation is calculated and handled by modifying the interconnection line.

[0133] S104. Based on the optimal solution, the new energy utilization rate of the target area power grid is calculated to realize the new energy consumption measurement.

[0134] As a possible implementation manner, the embodiment of the present invention can calculate the new energy utilization rate based on the following formula.

[0135] Utilization rate of centralized photovoltaic power generation cpv It can be expressed as:

[0136]

[0137] The utilization rate of new energy can be expressed as:

[0138]

[0139] Among them, η cpv is the utilization rate of centralized photovoltaics, and η is the utilization rate of new energy.

[0140] The present invention provides a new energy consumption calculation method and device that takes into account the dual growth of electric power and electricity. Based on power grid power information, historical operation data and forecast plan data, the conventional power supply operation mode of the target area power grid, the new energy generation sequence, the load sequence, the tie line sequence and other multivariate data are constructed to establish a new energy consumption calculation model that takes into account the dual growth of electric power and electricity, and adopts a mixed integer linear programming algorithm to obtain the optimal solution, and then calculate the new energy utilization rate of the target area power grid to achieve new energy consumption calculation. The present invention considers multiple factors to comprehensively and comprehensively calculate the new energy consumption, so that the new energy consumption calculation result conforms to the actual situation of the power grid and improves the accuracy of the new energy consumption calculation result.

[0141] The present invention is based on power grid information, historical operation data, and forecast plan data, and proposes a new energy consumption calculation method that takes into account the influence of multiple factors such as conventional power supply operation mode, new energy power generation sequence, load sequence, and interconnection line sequence. The conditions are comprehensive and more in line with the actual situation. The model established by the present invention is a mixed integer linear programming model, which is more suitable for the calculation and solution of long-term, multi-regional complex models, and realizes the convenient establishment and rapid optimization of power grid models. The full-caliber load sequence construction method proposed in the present invention takes into account the dual growth of electricity and electricity, which is more in line with actual needs. The present invention also proposes an unsolvable data processing method, which judges the power deviation by the startup capacity of adjacent time periods, and adjusts the interconnection line power to achieve balance, solving the problem of complex unsolvable data processing faced by new energy consumption calculation.

[0142] For example, the present invention takes the calculation data of new energy consumption in a certain month and a certain region as an example. The installed capacity of new energy in this region is 4.6 million kilowatts of centralized wind power, 13 million kilowatts of centralized photovoltaic power, and 18 million kilowatts of distributed photovoltaic power. The number of wind power resource hours is 206, the number of photovoltaic resource hours is 75, and the conventional power supply capacity and related information are not repeated.

[0143] Table 1 is the time-sharing schedule of the interconnection lines. According to the external power receiving plan, the time-sharing planned output and peak-flat-valley coefficient of the area can be obtained as shown in Table 1 (taking a certain day as an example). The area contains 5 interconnection lines. It is assumed that the peak-flat-valley plan of each interconnection line is the same, which is "1-7 o'clock low valley (0.5), 8 o'clock flat peak, 9-11 o'clock peak, 12-16 o'clock flat peak, 17-20 o'clock peak, 21-22 o'clock flat peak, 23-24 o'clock low valley".

[0144] Table 1

[0145] Through the above method, the integrated tie line schedule sequence can be obtained, taking the same period as an example. Table 2 shows the tie line time-sharing schedule.

[0146] Table 2

[0147] time Tie line sequence 2024 / 1 / 1 0:00 2750.5 2024 / 1 / 1 1:00 2750.5 2024 / 1 / 1 2:00 2750.5 2024 / 1 / 1 3:00 2750.5 2024 / 1 / 1 4:00 2750.5 2024 / 1 / 1 5:00 2750.5 … 2024 / 1 / 1 18:00 5501 2024 / 1 / 1 19:00 5501 2024 / 1 / 1 20:00 4125.75 2024 / 1 / 1 21:00 4125.75 2024 / 1 / 1 22:00 2750.5 2024 / 1 / 1 23:00 2750.5

[0148] The arrangement of the operation mode of the whole network thermal power in the unified dispatching and purchasing power capacity table of the relevant month and the ten-day power balance table provided by the present invention can obtain the maximum and minimum number of various types of thermal power units in each period. Based on the verification results of the minimum start-up mode output range of the thermal power unit and the unified dispatching and purchasing power capacity table, the maximum and minimum technical output coefficients of different types of units are obtained by distinguishing the heating period and the non-heating period as the boundary conditions for the operation of the thermal power unit. Taking a certain day as an example, the planned sequence of the thermal power unit is shown in Table 3. Table 3 is the operation mode sequence of the thermal power unit, including the maximum and minimum start-up numbers and the maximum and minimum operation mode parts.

[0149] Table 3

[0150]

[0151] By the maximum and minimum number of startup units and the ratio of the maximum and minimum operating modes to the unit capacity The specific operating power of thermal power units can be expressed as:

[0152]

[0153] in, is the actual operating power of the thermal power unit at time t, is the capacity of the m-type thermal power unit, It is the ratio of the number of thermal power units in operation and the operating mode to the unit capacity at time t.

[0154] Based on the theoretical output sequence and installed capacity of centralized wind power and photovoltaic power, combined with the estimated annual wind and solar resource utilization hours, the estimated monthly centralized wind power and photovoltaic resource sequence is obtained, as shown in Table 4. Multiplying by the installed capacity can obtain the theoretical output sequence of wind power and photovoltaic power. Figure 2Schematic diagram for calculating the monthly theoretical output of centralized photovoltaic power plants. Figure 3 Schematic diagram for calculating monthly theoretical wind power output.

[0155] Table 4

[0156] time Photovoltaic sequence Wind power series 2024 / 1 / 1 0:00 0 0.079512941 2024 / 1 / 1 1:00 0 0.081271922 2024 / 1 / 1 2:00 0 0.077293983 2024 / 1 / 1 3:00 0 0.098044542 2024 / 1 / 1 4:00 0 0.132363018 2024 / 1 / 1 5:00 0 0.135681444 … 2024 / 1 / 1 18:00 3.16E-04 0.156394232 2024 / 1 / 1 19:00 1.31E-03 0.155242528 2024 / 1 / 1 20:00 0 0.143058735 2024 / 1 / 1 21:00 0 0.14742833 2024 / 1 / 1 22:00 0 0.155287326 2024 / 1 / 1 23:00 0 0.161939118

[0157] The present invention takes the full-caliber load curve of a certain month as a basis, and according to the calculated full-caliber daily maximum load forecast power of the month and the full-caliber load forecast electricity as the goal, iteratively calculates the full-caliber load sequence that takes into account the dual growth of power and electricity. Figure 4 The following is a schematic diagram of the iteration process curve for calculating the monthly full-caliber load sequence. The iteration process of a certain day can be obtained by Figure 4 The process shown shows that, it can be seen that, with the maximum predicted power unchanged, the predicted power is gradually approaching the target value. Figure 5 The present invention calculates the full-caliber load curve of the month by finally obtaining the full-caliber load sequence of the month. Figure 5 shown.

[0158] Table 5 shows the estimated new energy consumption in the estimated month. Figure 6 Schematic diagram of the curve for calculating the monthly new energy abandonment situation. Figure 7 This is a schematic diagram of the curve for calculating the monthly thermal power generation capacity. Figure 8 Schematic diagram of the monthly thermal power output curve.

[0159] The present invention uses the new energy consumption calculation method to calculate the positive reserve capacity of 1700 MW and the negative reserve of 300 MW. The above model is solved by calling the CPLEX algorithm with the help of the YALMIP solver in MATLAB. The calculation results are shown in Table 5 and the power abandonment results are shown in Figure 6 As shown in the specific operation process, the conventional power output and startup capacity are as follows Figure 7 and Figure 8 shown.

[0160] Table 5

[0161]

[0162]

[0163] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0164] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0165] Fig. 9 The schematic diagram of the structure of a new energy consumption calculation device taking into account the double growth of electric power and electricity provided by an embodiment of the present invention is shown. The simulation test device 200 includes a communication module 201 and a processing module 202 .

[0166] The communication module 201 is used to obtain multivariate data of the target area power grid, where the multivariate data includes power grid power information, historical operation data and forecast plan data.

[0167] Processing module 202 is used to establish power generation and load sequences based on multivariate data, and to construct a new energy consumption calculation model; the new energy consumption calculation model takes the maximum new energy power generation as the objective function, and takes the power balance constraint and the equipment attribute constraint as the constraint conditions; based on the new energy consumption calculation model, a mixed integer linear programming algorithm is used to solve and obtain the optimal solution; the optimal solution includes the power generation of various types of new energy in each unit time period within the measurement period; based on the optimal solution, the new energy utilization rate of the target area power grid is calculated to realize the new energy consumption calculation.

[0168] Fig.10 Schematic diagram of the structure of a terminal device provided by an embodiment of the present invention. Fig.10 As shown, the terminal device 300 of this embodiment includes: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program 303, the steps in the above-mentioned method embodiments are implemented, for example Figure 1 Alternatively, when the processor 301 executes the computer program 303, the functions of each module / unit in the above-mentioned device embodiments are realized, for example, Fig. 9 The functions of the communication module 201 and the processing module 202 are shown.

[0169] Exemplarily, the computer program 303 may be divided into one or more modules / units, which are stored in the memory 302 and executed by the processor 301 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 303 in the terminal device 300. For example, the computer program 303 may be divided into Fig. 9 The communication module 201 and the processing module 202 are shown.

[0170] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0171] The memory 302 may be an internal storage unit of the terminal device 300, such as a hard disk or memory of the terminal device 600. The memory 302 may also be an external storage device of the terminal device 300, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 300. Further, the memory 302 may also include both an internal storage unit of the terminal device 300 and an external storage device. The memory 302 is used to store the computer program and other programs and data required by the terminal. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0172] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A new energy consumption calculation method taking into account the dual growth of electric power and electricity, characterized in that: include: Acquire multivariate data of the target area power grid, wherein the multivariate data includes power grid power information, historical operation data, and forecast plan data; Based on the multivariate data, a power generation and load sequence is established, and a new energy consumption calculation model is constructed; the new energy consumption calculation model takes the maximum power generation of new energy as the objective function, and takes power balance constraints and equipment attribute constraints as constraints; Based on the new energy consumption calculation model, a mixed integer linear programming algorithm is used to solve and obtain an optimal solution; the optimal solution includes the power generation of various types of new energy in each unit time period within the measurement period; Based on the optimal solution, the new energy utilization rate of the target area power grid is calculated to achieve new energy consumption measurement.

2. The new energy consumption calculation method taking into account the double growth of electric power and electricity according to claim 1 is characterized in that: The power grid information includes power grid information and power supply information; the power grid information includes peak, flat and valley time period information of each tie line and regional section information; the power supply information includes equipment attribute information of conventional thermal power units, equipment attribute information of electrochemical energy storage, and equipment attribute information of pumped storage power stations; the historical operation data includes the full-caliber load power sequence of the previous measurement year, the wind power theoretical data of the previous N years, and the photovoltaic theoretical data of the previous N years; the forecast plan data includes the daily power forecast data of the current measurement year, the monthly power forecast data, the monthly wind power installed capacity, the monthly photovoltaic installed capacity, the wind power forecast resource hours, the photovoltaic forecast resource hours, and the monthly plan sequence of the tie line; The generation and load sequences are established based on the multivariate data, and a new energy consumption estimation model is constructed, including: Based on the power grid information, determine the planning sequence of the tie line in each time period; Based on the power supply information, determining the planned sequence of each time period of the conventional thermal power generation unit; Based on the historical operation data and the forecast plan data, power generation forecast is performed to determine the output sequence of photovoltaic power generation in each period and the output sequence of wind power generation in each period; Determine the full-caliber load sequence for each period based on the historical operation data and forecast plan data; The new energy consumption calculation model is constructed based on the planning sequence of the interconnection line in each time period, the planning sequence of conventional thermal power units in each time period, the output sequence of photovoltaic power in each time period, the output sequence of wind power in each time period, and the full-caliber load sequence in each time period.

3. The new energy consumption calculation method taking into account the double growth of electric power and electricity according to claim 2 is characterized in that: The new energy consumption calculation model is constructed based on the plan sequence of each period of the tie line, the plan sequence of each period of conventional thermal power units, the output sequence of each period of photovoltaic power generation, the output sequence of each period of wind power generation, and the full-caliber load sequence of each period, including: Constructing the objective function based on the photovoltaic power output sequence in each time period and the wind power output sequence in each time period; The power balance constraint is constructed based on the plan sequence of the tie line in each period, the plan sequence of the conventional thermal power unit in each period, the output sequence of the photovoltaic power in each period, the output sequence of the wind power in each period and the full-caliber load sequence in each period; Based on the power supply information, construct the device attribute constraints, wherein the device attribute constraints include upper and lower limit constraints and ramp rate constraints of conventional thermal power units, system positive and negative standby constraints, new energy operation constraints, and energy storage operation constraints; Based on the objective function, the power balance constraint and the equipment property constraint, the new energy consumption estimation model is constructed.

4. The new energy consumption calculation method taking into account the double growth of electric power and electricity according to claim 2 is characterized in that: The power generation forecast is performed based on the historical operation data and the forecast plan data to determine the photovoltaic power output sequence in each time period and the wind power output sequence in each time period, including: Based on the theoretical photovoltaic data of the previous N years and the monthly installed capacity of photovoltaic power generation, the average value of the photovoltaic resource sequence for each month is obtained; Based on the average value of the photovoltaic resource sequence in each month and the photovoltaic predicted resource hours, determine the photovoltaic output sequence in each period; Based on the theoretical data of wind power in the previous N years and the monthly installed capacity of wind power, the average value of the wind power resource sequence for each month is obtained; Based on the average value of the wind power resource sequence in each month and the predicted wind power resource hours, the wind power output sequence in each time period is determined.

5. The new energy consumption calculation method taking into account the double growth of electric power and electricity according to claim 2 is characterized in that: Determining the full-caliber load sequence for each period based on the historical operation data and the forecast plan data includes: Step 1: Based on the full-caliber load power sequence of the previous calculation year, normalization processing is performed to obtain a daily normalized load sequence; Step 2: Based on the daily normalized load sequence and the daily power forecast data of the current calculation year, the power conversion of the daily full-caliber load is performed to obtain a power conversion sequence; Step 3: Based on the power conversion sequence and the monthly power forecast data, monthly power conversion is performed to obtain a power conversion sequence; Step 4: Based on the electricity conversion sequence and the daily full-caliber maximum predicted load, calculate the deviation between the daily maximum load and the maximum predicted load; Step 5: If the deviation is less than or equal to the set threshold, the electricity conversion sequence in the current iteration process is determined as the full-caliber load sequence of each time period, and the iteration process is exited. If the deviation is greater than the set threshold, steps 1 to 5 are repeated until the iteration process is exited.

6. The new energy consumption calculation method taking into account the double growth of electric power and electricity according to claim 5 is characterized in that: The daily normalized sequence of loads and the daily power forecast data of the current calculation year are used to convert the power of the full-caliber loads on a daily basis to obtain the power conversion sequence, including: Based on the following formula, the power conversion sequence is determined; Among them, P load-p (t) is the power conversion sequence, is the daily normalized load series, It is the maximum daily forecast load of all scopes in the daily electricity forecast data of the current calculation year.

7. The new energy consumption calculation method taking into account the double growth of electric power and electricity according to claim 5 is characterized in that: The method of converting the electric power monthly based on the electric power conversion sequence and the monthly electric power forecast data to obtain the electric power conversion sequence includes: Based on the following formula, the electricity conversion sequence is determined; Among them, P load-e (t) is the electricity conversion sequence, P load-p (t) is the power conversion sequence, It is the monthly electricity consumption forecast data, and N-day is the number of days in the measurement month.

8. The new energy consumption calculation method taking into account the double growth of electric power and electricity according to claim 1 is characterized in that: The method based on the new energy consumption calculation model adopts a mixed integer linear programming algorithm to solve and obtain the optimal solution, including: Using a mixed integer linear programming algorithm, the new energy consumption calculation model is solved to obtain a model output result; If the model output result is a solution, the solution in the model output result is determined as the optimal solution; If the model output result is that there is no solution, then determine the no-solution period, and based on the power generation data and load data of the no-solution period, determine the reason for the no-solution, wherein the no-solution reason includes that the load is too large and the power generation is insufficient, or the load is too small and the power generation is excessive; Based on the unsolvable reasons and the power generation conditions in the adjacent time periods of the unsolvable time period, recalculation is performed, and the plan sequence of each time period of the tie line is modified to obtain an updated plan sequence of each time period of the tie line.

9. The new energy consumption calculation method taking into account the double growth of electric power and electricity according to claim 8 is characterized in that: The recalculation based on the unsolvable reason and the power generation conditions of the adjacent time periods of the unsolvable time period is performed, and the plan sequence of each time period of the tie line is modified to obtain an updated plan sequence of each time period of the tie line, including: If the reason for the unsolvable problem is that the load is too large and the power generation is insufficient, then based on the maximum output data of the adjacent time periods of the unsolvable time period and the new energy consumption calculation model, recalculate to obtain a recalculated result, and based on the recalculated result, modify the plan sequence of each time period of the tie line with the power balance constraint as the goal, to obtain an updated plan sequence of each time period of the tie line; If the reason for the unsolvable problem is that the load is too small and the power generation is too large, then the calculation is recalculated based on the minimum output data of the adjacent time periods of the unsolvable time period and the new energy consumption calculation model to obtain a recalculated result. Based on the recalculated result and with the power balance constraint as the goal, the plan sequence of each time period of the interconnection line is modified to obtain an updated plan sequence of each time period of the interconnection line.

10. A new energy consumption calculation device that takes into account the dual growth of electric power and electricity, characterized in that: include: A communication module, used to obtain multivariate data of the target area power grid, wherein the multivariate data includes power grid power information, historical operation data and forecast plan data; A processing module is used to establish power generation and load sequences based on the multivariate data, and to construct a new energy consumption calculation model; the new energy consumption calculation model takes the maximum new energy power generation as the objective function, and takes the power balance constraint and the equipment attribute constraint as the constraint conditions; based on the new energy consumption calculation model, a mixed integer linear programming algorithm is used to solve and obtain the optimal solution; the optimal solution includes the power generation of various types of new energy in each unit time period within the measurement period; based on the optimal solution, the new energy utilization rate of the target area power grid is calculated to realize the new energy consumption calculation.

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