CN111130107A - Power grid load prediction method and device - Google Patents

Power grid load prediction method and device Download PDF

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CN111130107A
CN111130107A CN202010067364.7A CN202010067364A CN111130107A CN 111130107 A CN111130107 A CN 111130107A CN 202010067364 A CN202010067364 A CN 202010067364A CN 111130107 A CN111130107 A CN 111130107A
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load
power
formula
demand side
input power
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丁斌
邢志坤
王帆
袁博
赵树军
唐宝锋
李振伟
张�浩
杨行方
刘鹏
孟斌
赵路新
张海涛
连浩然
闫浩然
刘瑞麟
刘东亮
孙岩
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Xiongan New Area Power Supply Company State Grid Hebei Electric Power Co
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    • H—ELECTRICITY
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Abstract

The invention discloses a power grid load prediction method and a power grid load prediction device. Wherein, the method comprises the following steps: predicting the space load of the power grid; predicting the loads of various load devices in the power grid and the input power of various power supply devices to the power grid, wherein the loads comprise electric automobile loads and demand side loads; the input power comprises photovoltaic input power, fan input power and energy storage input power; and determining a load prediction result of the power grid based on the space load. The device includes: the first prediction module is used for predicting the space load of the power grid; the second prediction module is used for predicting electric automobile load, demand side load, photovoltaic input power, fan input power and energy storage input power in the power grid; and the determining module is used for determining the load prediction result of the power grid. According to the method and the device, the technical problem that the load prediction accuracy requirement of the power grid cannot be met due to few consideration factors in the power grid load prediction method in the related technology is solved.

Description

Power grid load prediction method and device
Technical Field
The invention relates to the field of electric power, in particular to a power grid load prediction method and device.
Background
The power distribution system is an important component of the power system and an important intermediate link between the power transmission network and the users. In traditional power distribution network planning, a power distribution network and a user side stably play a relationship with supply and demand. However, in recent years, with the continuous development of economy and the increase of social electricity demand, the maximum load utilization hours of the power grid continuously decrease, and the problem of peak load is increasingly prominent. In the process, the appearance of the novel load also puts new requirements on the operation control of the power distribution network, such as electric automobiles, electric heating and the like. On the other hand, the increasing permeability of novel energy sources such as distributed power sources and energy storage is also the development trend of the current power distribution network. Compared with a traditional power generation mode, the distributed power supply has the characteristics of high randomness, strong volatility, uncontrollable input power and the like, cannot be treated as same as a conventional unit in power planning, and needs an active coordination control capability introduced by an original power distribution system to the distributed power supply. These have all prompted the transition of the original power distribution system, i.e., the evolution towards the active power distribution system.
The related art generally adopts the following method to predict the load of the power grid:
1. space load prediction method: the method is based on the division of regional plots, gives load density indexes of each type of plot property, completes the load prediction of each plot, and then summarizes the load prediction results of the whole region.
2. A mathematical model method: the method is typically based on historical data and uses mathematical models such as regression analysis, trend extrapolation, polynomials, etc. to make the prediction.
3. Based on a load classification prediction method: firstly, dividing the load into an uncontrollable load, a controllable load and an adjustable load, and further subdividing the load into a friendly load and a non-friendly load; then introducing a load response coefficient (lambda) concept, respectively establishing mathematical models, and then predicting by adopting a typical distribution network load prediction method of distance before distance.
At present, research aiming at a load prediction method is very intensive, and loads in a future area are predicted in different modes. However, with the development and utilization of comprehensive energy and the continuous emergence of new loads, the traditional prediction method has low accuracy of power grid load prediction due to few considered factors, and cannot meet the current requirements.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a power grid load prediction method and a power grid load prediction device, which at least solve the technical problem that the power grid load prediction requirement cannot be met due to few consideration factors in a power grid load prediction method in the related technology.
According to an aspect of an embodiment of the present invention, there is provided a power grid load prediction method, including: predicting the space load of the power grid, and the load of various load devices and the input power of various power supply devices in the power grid to the power grid, wherein the various loads comprise at least one of the following: electric vehicle load, demand side load; the plurality of input powers includes at least one of: photovoltaic input power, fan input power and energy storage input power; and determining a load prediction result of the power grid based on the space load according to the various loads and the various input powers.
Optionally, determining a load prediction result of the power grid based on the space load according to the plurality of loads and the plurality of input powers includes: determining the load prediction result according to a load prediction formula, wherein the load prediction formula is as follows:
Fa＝F0-Fp-Fw+Fe+Fd-Fq
in the formula, FaLoad prediction results under the active power distribution network are obtained; f0For space load, FpFor photovoltaic input power, FwFor fan input power, FeFor electric vehicle load, FdFor demand side load, FqPower is input for energy storage.
Optionally, predicting various loads in the power grid includes: predicting the space load; in the case of predicting the space load of a distant view year, predicting the space load of the distant view year by a distant view year formula, wherein the distant view year formula is as follows:
Figure BDA0002376371710000021
in the formula, F0' is the prediction result of the space load in the distant view year; m is the number of land used in the grid of the space; diA floor area-load characteristic curve for a unit building of class i buildings; siIs the floor space of the i-th building;
or, in the case of predicting the space load of the middle year, predicting the space load of the middle year by a middle year formula, wherein the middle year is a year between the current year and the distant view year, and the middle year formula is as follows:
F0″＝Y(t)·F0′
wherein Y is the percentage of the space load of the middle year to the space load of the long-range view year; t is the number of years from the present year;
Figure BDA0002376371710000022
in the formula, A is the growth coefficient of the load in the middle year, and e is a natural constant.
Optionally, predicting various loads in the power grid includes: predicting a photovoltaic input power in the presence of the photovoltaic input power in the grid; determining the photovoltaic input power according to a photovoltaic power formula, wherein the photovoltaic power formula is as follows:
Figure BDA0002376371710000031
in the formula, FpThe photovoltaic input power is obtained, and S is actually measured solar illumination intensity; t is the actually measured temperature of the battery plate; srefIs the intensity of light under standard conditions, PrefIs a labelOutput power under quasi-conditions; t isrefIs the cell plate temperature under standard conditions; and gamma is a preset maximum power temperature coefficient of the photovoltaic equipment.
Optionally, predicting various loads in the power grid includes: predicting the input power of the fan under the condition that the input power of the fan exists in the power grid; determining the fan input power according to a fan power formula, wherein the fan power formula is as follows:
Fw＝min(Ne，Pmax)
in the formula, NeFor optimum installed capacity of the fan, PmaxThe power generation load without regard to the capacity of the wind turbine;
Figure BDA0002376371710000032
in the formula, K is a wind turbine power conversion coefficient; caIs the air height density conversion coefficient; ctIs the air temperature density correction coefficient; s is the wind sweeping area of the wind turbine blade;
Figure BDA0002376371710000033
to predict annual average wind speed, η for full fan efficiency,
Pmax＝K×Ca×Ct×S×vt 3×η
in the formula, vtIs the real-time wind speed.
Optionally, predicting various loads in the power grid includes: predicting an electric vehicle load in the presence of the electric vehicle load in the grid; determining the electric vehicle load according to an electric vehicle load formula, wherein the electric vehicle load formula is as follows:
Fe＝N·q·D1·k1+N·(1-q)·D2·k2
in the formula, FeIs the electric automobile load; n is the number of the electric automobile parking spaces; q is a slow charge/charge ratio; d1The load index of the slow filling pile is obtained; k is a radical of1The pile slow-filling synchronization rate is obtained; d2For quick pile filling load index；k2The pile filling is performed quickly.
Optionally, predicting various loads in the power grid includes: predicting a demand side load when the demand side load is present in the grid; under the condition of determining the consumption reduction rate of i' class demand side resources of a user, determining a first single demand side load according to a first single demand side load formula, wherein the first single demand side load formula is as follows:
Figure BDA0002376371710000041
in the formula,. DELTA.Pmax，i′Representing consumption reduction load under the action of i' type demand side resources αEE，i′Representing consumption reduction rate of resources on the i' class demand side of the user, t representing hours in the prediction period, βLD，i′Representing the load rate of the user under the action of the load-class resources; q0，preRepresenting that the user does not consider the electricity consumption of the resource at the demand side in the forecast year;
Q0，pre＝P0，pre·Tmax
in the formula, P0，preNot considering the maximum load of demand side resources for the user; t ismaxA number of hours of maximum load utilization for the user;
or, under the condition that the i 'type demand side resource of the user is adjustable, determining a second single demand side load of the i' type demand side resource according to a second single demand side load formula, where the second single demand side load formula is:
ΔPmax，i＇＝P0，pre·μi＇·σi＇·ρi′，j
in the formula, mui′For the total controllable load proportion, σ, of the i' class demand side resourcesi′Adjustable demand side resource proportion, ρ, for i' type demand side resourcesi′，jThe influence coefficient of the response measure j on the i' type demand side resource; determining the demand side load according to a demand side load formula, wherein the demand side load formula is as follows:
Figure BDA0002376371710000042
where n represents the number of demand side resources owned by the user and ξ is the preset demand side load factor.
Optionally, predicting various loads in the power grid includes: predicting the energy storage input power; under the condition that the energy storage equipment is charged, predicting the energy storage input power according to a charging energy storage input power formula, wherein the charging energy storage input power formula is as follows:
Fq＝-P1(t)
in the formula, P1(t) is the charging power of the energy storage device; p1(t) satisfies the following formula:
E(t)＝(1-τ)E(t-1)+P1(t)η1Δt
wherein E (t) is the electric quantity of the energy storage device in a t period, E (t-1) is the electric quantity of the energy storage device in a t-1 period before the t period, η1The charge-discharge efficiency of the energy storage device; τ is the self-discharge rate of the energy storage device;
under the condition that the energy storage equipment is discharged, predicting the energy storage input power according to a discharge energy storage input power formula, wherein the discharge energy storage input power formula is as follows:
Fq＝P2(t)
in the formula, P2(t) is the discharge power of the energy storage device; p2(t) satisfies the following formula:
Figure BDA0002376371710000051
according to another aspect of the embodiments of the present invention, there is also provided a power grid load prediction apparatus, including: the prediction module is used for predicting the space load of the power grid, the load of various load devices in the power grid and the input power of various power supply devices to the power grid, wherein the various loads comprise at least one of the following: electric vehicle load, demand side load; the plurality of input powers includes at least one of: photovoltaic input power, fan input power and energy storage input power; and the determining module is used for determining a load prediction result of the power grid based on the space load according to the various loads and the various input powers.
Optionally, the determining module includes: a determining unit, configured to determine the load prediction result according to a load prediction formula, where the load prediction formula is as follows:
Fa＝F0-Fp-Fw+Fe+Fd-Fq
in the formula, FaLoad prediction results under the active power distribution network are obtained; f0For space load, FpFor photovoltaic input power, FwFor fan input power, FeFor electric vehicle load, FdFor demand side load, FqPower is input for energy storage.
Optionally, the prediction module includes: a first prediction unit to predict a space load;
the first prediction unit is configured to predict a spatial load of a distant view year by a distant view year equation in a case where the spatial load of the distant view year is predicted, wherein the distant view year equation is:
Figure BDA0002376371710000052
in the formula, F0' is the prediction result of the space load in the distant view year; m is the number of land used in the grid of the space; diA floor area-load characteristic curve for a unit building of class i buildings; siIs the floor space of the i-th building;
the first prediction unit is further configured to predict the space load of the middle year by a middle year formula in a case where the space load of the middle year is predicted, wherein the middle year is a year between a present year and a distant view year, and the middle year formula is:
F0″＝Y(t)·F0′
wherein Y is the percentage of the space load of the middle year to the space load of the long-range view year; t is the number of years from the present year;
Figure BDA0002376371710000061
in the formula, A is the growth coefficient of the load in the middle year, and e is a natural constant.
Optionally, the prediction module includes: a second prediction unit for predicting the photovoltaic input power in the presence of the photovoltaic input power in the grid; determining the photovoltaic input power according to a photovoltaic power formula, wherein the photovoltaic power formula is as follows:
Figure BDA0002376371710000062
in the formula, FpThe photovoltaic input power is obtained, and S is actually measured solar illumination intensity; t is the actually measured temperature of the battery plate; srefIs the intensity of light under standard conditions, PrefIs the output power under standard conditions; t isrefIs the cell plate temperature under standard conditions; and gamma is a preset maximum power temperature coefficient of the photovoltaic equipment.
Optionally, the prediction module includes: a third prediction unit, configured to predict the input power of the wind turbine when the input power of the wind turbine exists in the power grid; determining the fan input power according to a fan power formula, wherein the fan power formula is as follows:
Fw＝min(Ne，Pmax)
in the formula, NeFor optimum installed capacity of the fan, PmaxThe power generation load without regard to the capacity of the wind turbine;
Figure BDA0002376371710000063
in the formula, K is a wind turbine power conversion coefficient; caFor changing the air height densityCalculating coefficients; ctIs the air temperature density correction coefficient; s is the wind sweeping area of the wind turbine blade;
Figure BDA0002376371710000064
to predict annual average wind speed, η for full fan efficiency,
Pmax＝K×Ca×Ct×S×vt 3×η
in the formula, vtIs the real-time wind speed.
Optionally, the prediction module includes: a fourth prediction unit for predicting an electric vehicle load in the presence of the electric vehicle load in the power grid;
the fourth prediction unit is used for determining the electric vehicle load according to an electric vehicle load formula, wherein the electric vehicle load formula is as follows:
Fe＝N·q·D1·k1+N·(1-q)·D2·k2
in the formula, FeIs the electric automobile load; n is the number of the electric automobile parking spaces; q is a slow charge/charge ratio; d1The load index of the slow filling pile is obtained; k is a radical of1The pile slow-filling synchronization rate is obtained; d2The load index of the quick filling pile is obtained; k is a radical of2The pile filling is performed quickly.
Optionally, the prediction module includes: a fifth prediction unit for predicting a demand side load in the presence of the demand side load in the grid;
the fifth prediction unit is configured to determine a first single demand side load according to a first single demand side load formula under the condition that the consumption reduction rate of the i' class demand side resource of the user is determined, where the first single demand side load formula is:
Figure BDA0002376371710000071
in the formula,. DELTA.Pmax，i′Representing consumption reduction load under the action of i' type demand side resources αEE，i′Represents the aboveConsumption reduction rate of i' class demand side resource of user, t represents hour number in prediction period, βLD，i′Representing the load rate of the user under the action of the load-class resources; q0，preRepresenting that the user does not consider the electricity consumption of the resource at the demand side in the forecast year;
Q0，pre＝P0，pre·Tmax
in the formula, P0，preNot considering the maximum load of demand side resources for the user; t ismaxA number of hours of maximum load utilization for the user;
the fifth prediction unit is further configured to determine, according to a second single demand-side load formula, a second single demand-side load of the i 'class of demand-side resources under a condition that the i' class of demand-side resources of the user is controllable, where the second single demand-side load formula is:
ΔPmax，i′＝P0，pre·μi′·σi＇·ρi′，j
in the formula, mui′For the total controllable load proportion, σ, of the i' class demand side resourcesi′Adjustable demand side resource proportion, ρ, for i' type demand side resourcesi′，jThe influence coefficient of the response measure j on the i' type demand side resource; determining the demand side load according to a demand side load formula, wherein the demand side load formula is as follows:
Figure BDA0002376371710000081
where n represents the number of demand side resources owned by the user and ξ is the preset demand side load factor.
Optionally, the prediction module includes: a sixth prediction unit configured to predict the energy storage input power when the energy storage input power exists in the power grid;
the sixth prediction unit is configured to predict the energy storage input power according to a charging energy storage input power formula when the energy storage device is charged, where the charging energy storage input power formula is:
Fq＝-P1(t)
in the formula, P1(t) is the charging power of the energy storage device; p1(t) satisfies the following formula:
E(t)＝(1-τ)E(t-1)+P1(t)η1Δt
wherein E (t) is the electric quantity of the energy storage device in a t period, E (t-1) is the electric quantity of the energy storage device in a t-1 period before the t period, η1The charge-discharge efficiency of the energy storage device; τ is the self-discharge rate of the energy storage device;
the sixth prediction unit is further configured to predict the energy storage input power according to a discharge energy storage input power formula when the energy storage device is discharged, where the discharge energy storage input power formula is:
Fq＝P2(t)
in the formula, P2(t) is the discharge power of the energy storage device; p2(t) satisfies the following formula:
Figure BDA0002376371710000082
according to another aspect of the embodiments of the present invention, there is also provided a storage medium, where the storage medium includes a stored program, and when the program runs, the apparatus where the storage medium is located is controlled to execute the power grid load prediction method described in any one of the above.
According to another aspect of the embodiments of the present invention, there is also provided a processor, configured to execute a program, where the program executes to perform the grid load prediction method described in any one of the above.
In the embodiment of the invention, the space load of the power grid is predicted; predicting loads of various load devices in the power grid and input power of various power supply devices to the power grid, wherein the various loads comprise at least one of the following: electric vehicle load, demand side load; the plurality of input powers includes at least one of: photovoltaic input power, fan input power and energy storage input power; according to various loads and various input powers, a mode of determining a load prediction result of a power grid based on space load is adopted, the predicted space load of the power grid is corrected by rechecking and predicting the various loads in the power grid and predicting the input power of various power supply equipment, so that the aim of accurately determining the load prediction result of the power grid is fulfilled by considering more factors through the various loads and the various power supply equipment, the technical effect of improving the accuracy of power grid load prediction is achieved, and the technical problem that the demand of power grid load prediction cannot be met due to less consideration factors in a power grid load prediction method in the related technology is solved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a method for grid load prediction according to an embodiment of the present invention;
fig. 2 is a flowchart of a space load calculation method according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present invention, there is provided a method embodiment of a grid load forecasting method, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a flowchart of a method for predicting a load of a power grid according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, predicting the space load of the power grid, the load of various load devices in the power grid and the input power of various power supply devices to the power grid, wherein the various loads comprise at least one of the following: electric vehicle load, demand side load; the plurality of input powers includes at least one of: photovoltaic input power, fan input power and energy storage input power;
and step S104, determining a load prediction result of the power grid based on the space load according to various loads and various input powers.
Through the steps, predicting the space load of the power grid, and the load of various load devices and the input power of various power supply devices in the power grid to the power grid, wherein the various loads comprise at least one of the following: electric vehicle load, demand side load; the plurality of input powers includes at least one of: photovoltaic input power, fan input power and energy storage input power; according to various loads and various input powers, a mode of determining a load prediction result of a power grid based on space load is adopted, the predicted space load of the power grid is corrected by rechecking and predicting the various loads in the power grid and predicting the input power of various power supply equipment, so that the aim of accurately determining the load prediction result of the power grid is fulfilled by considering more factors through the various loads and the various power supply equipment, the technical effect of improving the accuracy of power grid load prediction is achieved, and the technical problem that the demand of power grid load prediction cannot be met due to less consideration factors in a power grid load prediction method in the related technology is solved.
With the more and more severe change of the power grid load and the more and more high peak load, the prediction of the power grid load can improve the capability of the power grid to deal with the power grid load with the severe change to a certain extent, thereby improving the stability and the reliability of the power grid. The power grid load prediction method in the related art comprises a space load prediction method, a mathematical model method and a load classification prediction method, which are used for predicting the power grid load from one side, and the change of the power grid load is intensified along with the development and utilization of comprehensive energy in a power grid and the outgoing lines of various novel loads, so that the traditional power grid load prediction method has the problems that the prediction cannot be comprehensively and effectively carried out, and the accuracy of the outgoing line power grid load prediction is low.
The method is combined with a space load prediction method, a mathematical model method and a load classification prediction method, multiple loads including space loads, electric automobile loads and demand side loads are predicted, multiple input powers including photovoltaic input power, fan input power and energy storage input power are predicted, and the loads of the power grid are predicted through the multiple loads and the multiple input powers.
The space load is a traditional load in the power grid predicted by a space load prediction method, the space load prediction method is a method for predicting the traditional load of the power grid, the traditional load in the power grid can be predicted by the space load prediction method, then a novel load is specially predicted, for example, the load of an electric vehicle, the load on a demand side and the like are taken as the novel load, and the prediction can be carried out by other methods, for example, a mathematical model method. The new load may also include other loads that may occur different from the conventional load, such as a load of a high-speed rail, a load of an electric vehicle, and the like.
Different from the traditional power grid, the new energy resource is started, so that the types of power supplies in the power grid are increased, the occupation ratio is increased, and the input power of the new energy resource power supply to the power grid is predicted. The new energy may include photovoltaic power generation, wind power generation, hydroelectric power generation, tidal power generation, bio-power generation, and the like. Under the condition that the traditional power sources in the power grid are different in types and exceed a certain proportion, the input power of the new energy to the power grid needs to be considered, and therefore the accuracy of power grid load prediction is improved.
The load prediction result of the power grid is determined according to various power grid loads and input power of various power supplies in the power grid, and the space load obtained by predicting the traditional load can be obtained through space prediction, so that the novel load is superposed, and the novel energy is reduced. Because the traditional load and the novel load all cause power output to the electric wire netting, are the load of electric wire netting, superpose, above-mentioned traditional load is the electric energy output of electric wire netting, and the novel energy is the electric energy input to the electric wire netting, consequently, when the prediction electric wire netting load, need subtract the input power of all kinds of novel energy.
In this embodiment, the plurality of loads includes at least one of: electric vehicle load, demand side load, the plurality of input powers comprising at least one of: photovoltaic input power, fan input power, and energy storage input power. Determining a load prediction result of the power grid according to the various loads and the various input powers comprises: determining a load prediction result according to a load prediction formula, wherein the load prediction formula is as follows:
Fa＝F0-Fp-Fw+Fe+Fd-Fq
in the formula, FaLoad prediction results under the active power distribution network are obtained; f0For space load, FpFor photovoltaic input power, FwFor fan input power, FeFor electric vehicle load, FdFor demand side load, FqPower is input for energy storage.
The order of the various loads and the various input powers described above may be determined according to the specific requirements in the grid. In this embodiment, the various loads and the various input powers may be according to the load prediction formula, Fa＝F0-Fp-Fw+Fe+Fd-FqThe order in (1) is predicted and determined, namely the space load F is predicted first0Predicting the photovoltaic input power F based on the space load under the condition of having the photovoltaic input power in the power gridpPredicting the input power F of the fan under the condition of the input power of the fan in the power gridwPredicting the electric vehicle load F in the case of electric vehicle loads in the networkePredicting a demand-side load F in the event of a demand-side load in the power griddPredicting the energy storage input power F in the case of an energy storage input power in the networkq. The coincidence prediction is based on the conventional space load F0Based on the consideration of the influence factors in various aspects on the space load F0Correction is made so that the space load F is first calculated0And predicting subsequent various loads and various input powers under the condition that the various loads and the various input powers exist in the power grid, wherein the various loads and the various input powers are sequenced according to the influence degree on the power grid load, and the influence on the power grid load is larger before the sequencing is carried out.
The degree of the above-mentioned influence on the grid load can be determined according to the load of this type or the ratio of the input power to the space load, and the larger the ratio is, the larger the influence is. The various loads have different influence degrees on the power grid in different power grid systems, and some power grid systems may have more developed wind power generation than photovoltaic power generation, so that the input power of the fan can be predicted firstly, then the input power of the photovoltaic can be predicted, and other loads and input power can be adjusted sequentially. In the case that a new load or a new energy source exists in the grid system and the occupancy exceeds a certain threshold, the new load or the new energy source can be incorporated into the load prediction formula, for example, the grid system in the coastal region may depend on tidal energy, so that the tidal energy input power can be added in the grid load prediction process, and the specific sequence can also be determined according to the tidal energy input power, various loads and the influence degree of various input powers on the grid system. Through the sequence, the importance of various loads and various input powers in the power grid load prediction can be more fully considered, so that the power grid load can be more reasonably and accurately predicted.
Optionally, predicting various loads in the power grid includes: predicting the space load; under the condition of predicting the space load of the distant view year, predicting the space load of the distant view year through a distant view year formula, wherein the distant view year formula is as follows:
Figure BDA0002376371710000121
in the formula, F0' is the prediction result of space load in the distant view year; m is the number of land used in the grid of the space; diA floor area-load characteristic curve for a unit building of class i buildings; siIs the floor space of the i-th building; the long-range year is a year far from the current present year, and the long-range year may be from the eleventh year to the fifteenth year, from the fifteenth year to the twentieth year, or from the eleventh year to the twentieth year.
The space load is based on the division of the region and the plot, the plot of the prediction region is divided into a plurality of grids, the load density index of the property of each type of land is given, the load prediction of each grid plot is completed, and then the load prediction results of the whole region are summarized. The types of the buildings are classified according to different classification principles, and classification results are different, for example, the buildings can be classified according to purposes and comprise civil buildings and industrial buildings, the civil buildings also comprise residential buildings and public-customs buildings, and the residential buildings comprise town houses, apartments, dormitories, villas, rural residences and the like. The public customs buildings comprise office buildings, education buildings, scientific research experiment buildings, commemorative buildings, medical buildings, commercial buildings, financial insurance buildings, traffic buildings, post and telecommunications buildings, other buildings and the like. In industrial buildings, can be used as raw materialThe product can be produced into various types including ferrous metallurgy buildings, textile industry buildings, mechanical industry buildings, chemical industry buildings, building material industry buildings, power industry buildings, light industry buildings, other buildings and the like. The method can also be classified according to the purposes of plants, such as main production plants, auxiliary production plants, power plants, auxiliary storage buildings and the like. The plant floor number classification can also be carried out according to plant floor numbers, including single-layer plants, multi-layer plants, mixed plants and the like. The production conditions in the production workshop can be classified according to the internal production conditions, and the production conditions comprise a hot workshop, a cold workshop, a constant temperature and humidity workshop and the like. Different types of buildings, the floor area and the load characteristic curve of which are different, can pass through the front part according to the floor area-load characteristic curve of each type of buildings
Figure BDA0002376371710000135
The area of the building, the load characteristics of the building are predicted, and the load of each grid plot is determined from the determined load of the building, thereby determining the space load.
Alternatively, in the case of predicting the space load of an intermediate year, the space load of the intermediate year is predicted by an intermediate year formula, wherein the intermediate year is a year between the current year and the prospective year, and the intermediate year is usually a year from the current year, and may be from the first year to the fifth year, or from the fifth year to the tenth year, or from the first year to the tenth year.
The formula for the middle year is:
F0″＝Y(t)·F0′
wherein Y is the percentage of the space load of the middle year to the space load of the long-range view year; t is the number of years from the present year;
Figure BDA0002376371710000131
in the formula, A is the growth coefficient of the load in the middle year, and e is a natural constant. The growth coefficient of the load in the middle year is calculated according to the load data of the specific year.
Optionally, predicting various loads in the power grid includes: predicting the photovoltaic input power under the condition that the photovoltaic input power exists in the power grid; determining the photovoltaic input power according to a photovoltaic power formula, wherein the photovoltaic power formula is as follows:
Figure BDA0002376371710000132
in the formula, FpThe photovoltaic input power is obtained, and S is actually measured solar illumination intensity; t is the actually measured temperature of the battery plate; srefIs the intensity of light under standard conditions, PrefIs the output power under standard conditions; t isrefIs the cell plate temperature under standard conditions; and gamma is a preset maximum power temperature coefficient of the photovoltaic equipment.
Optionally, predicting various loads in the power grid includes: predicting the input power of a fan under the condition that the input power of the fan exists in a power grid; determining the input power of the fan according to a fan power formula, wherein the fan power formula is as follows:
Fw＝min(Ne，Pmax)
in the formula, NeFor optimum installed capacity of the fan, PmaxThe power generation load without regard to the capacity of the wind turbine;
Figure BDA0002376371710000133
in the formula, K is a wind turbine power conversion coefficient; caIs the air height density conversion coefficient; ctIs the air temperature density correction coefficient; s is the wind sweeping area of the wind turbine blade;
Figure BDA0002376371710000134
to predict annual average wind speed, η for full fan efficiency,
Pmax＝K×Ca×Ct×S×vt 3×η
in the formula, vtIs the real-time wind speed.
The input power of the fan and the input power of the photovoltaic are predicted to the power grid through a mathematical model method.
Optionally, predicting various loads in the power grid includes: predicting the electric vehicle load under the condition that the electric vehicle load exists in the power grid; determining the electric automobile load according to an electric automobile load formula, wherein the electric automobile load formula is as follows:
Fe＝N·q·D1·k1+N·(1-q)·D2·k2
in the formula, FeIs the electric automobile load; n is the number of the electric automobile parking spaces; q is a slow charge/charge ratio; d1The load index of the slow filling pile is obtained; k is a radical of1The pile slow-filling synchronization rate is obtained; d2The load index of the quick filling pile is obtained; k is a radical of2The pile filling is performed quickly. The slow charging pile can be a charging pile which does not adopt a quick charging technology to charge, and the quick charging pile can be a charging pile which adopts a quick charging technology to charge.
Optionally, predicting various loads in the power grid includes: predicting a demand side load under the condition that the demand side load exists in a power grid; under the condition of determining the consumption reduction rate of the i' type demand side resources of the user, determining a first single demand side load according to a first single demand side load formula, wherein the first single demand side load formula is as follows:
Figure BDA0002376371710000141
in the formula,. DELTA.Pmax，i′Representing consumption reduction load under the action of i' type demand side resources αEE，i′Representing consumption reduction rate of resources on the i' class demand side of the user, t representing hours in the prediction period, βLD，i′Representing the load rate of the user under the action of the load-class resources; q0，preThe power consumption of the resources on the demand side is not considered by the user in the forecast year;
Q0，pre＝P0，pre·Tmax
in the formula, P0，preThe maximum load of the demand side resource is not considered for the user; t ismaxThe number of hours of maximum load utilization for the user;
or, under the condition that the i 'type demand side resources of the user can be regulated, determining a second single demand side load of the i' type demand side resources according to a second single demand side load formula, where the second single demand side load formula is as follows:
ΔPmax，i′＝P0，pre·μi′·σi′·ρi′，j
in the formula, mui′For the total controllable load proportion, σ, of the i' class demand side resourcesi′Adjustable demand-side resource proportion, rho, for i' class demand-side resourcesi′，jThe influence coefficient of the response measure j on the i' type demand side resource; determining a demand side load according to a demand side load formula, wherein the demand side load formula is as follows:
Figure BDA0002376371710000151
where n represents the number of demand side resources owned by the user and ξ is the preset demand side load factor.
And a load classification prediction method is adopted when the demand side load is predicted, and the first single demand side load is the load for determining the consumption reduction rate of the i' type demand side resource of the user. The second single demand side load is a load whose resources are controllable at the i' type demand side of the user.
The measures for the demand side resources can include improving the electricity utilization efficiency of equipment such as lighting, air conditioners, motors, electric heating, refrigeration and the like; cold accumulation, virtual heat, energy storage and the like change the electricity utilization mode; energy source replacement and complementary energy recovery; contract commitment interruptible load; building heat preservation and the like; the customer changes the consumption behavior; a self-contained power plant. Correspondingly, the demand side resource refers to a power grid demand side, that is, a node resource of a client, and mainly includes: the power consumption efficiency of equipment such as lighting, air conditioners, motors, electric heating, refrigeration and the like is improved, and the saved electric power and electric quantity are improved; cold accumulation, virtual heat, energy storage and the like change the electric power saved by the electricity utilization mode; reduced and saved electric power and electric quantity are replaced by energy and recovered by complementary energy; contract agreement can interrupt the electric power and electric quantity that the load saves; building heat preservation and the like are completed, so that the electric power and the electric quantity saved by electricity utilization are improved; the customers change consumption behaviors to reduce the electricity and the electric quantity saved by electricity utilization; the power and the electric quantity which are supplied by the power grid after the self-contained power plant participates in the dispatching, and the like.
Optionally, predicting various loads in the power grid includes: predicting the energy storage input power under the condition that the energy storage input power exists in the power grid; under the condition that the energy storage equipment is charged, predicting energy storage input power according to a charging energy storage input power formula, wherein the charging energy storage input power formula is as follows:
Fq＝-P1(t)
in the formula, P1(t) is the charging power of the energy storage device; p1(t) satisfies the following formula:
E(t)＝(1-τ)E(t-1)+P1(t)η1Δt
wherein E (t) is the electric quantity of the energy storage device in the t period, E (t-1) is the electric quantity of the energy storage device in the t-1 period before the t period, η1The charge-discharge efficiency of the energy storage device is obtained; τ is the self-discharge rate of the energy storage device;
under the condition that the energy storage equipment discharges, the energy storage input power is predicted according to a discharge energy storage input power formula, wherein the discharge energy storage input power formula is as follows:
Fq＝P2(t)
in the formula, P2(t) is the discharge power of the energy storage device; p2(t) satisfies the following formula:
Figure BDA0002376371710000161
in addition, for the energy storage device, considering the hardware characteristics of the energy storage device, the energy storage device is limited by the charging and discharging power, and the following charging and discharging constraints are provided:
0≤P2，P1≤P2
in the formula, P1Representing the minimum value, P, of the charge and discharge power of the energy storage device2Representing the maximum value of the charge and discharge power of the energy storage device;
simultaneously in order to provide the emergent reserve of active power distribution network, the state of charge restraint of setting the energy storage as follows:
E1≤E≤E2
in the formula, E1And E2And E represents the maximum capacity and the minimum capacity of the storage battery, and is the electric quantity of the energy storage equipment. Therefore, the electric quantity and the charging and discharging power of the energy storage device are restrained, and the accuracy of the calculated result is higher.
It should be noted that this embodiment also provides an alternative implementation, which is described in detail below.
In the embodiment, under an active power distribution network, the load prediction is completed in the aspects of analyzing the input power of a distributed power supply to the power grid, future energy storage arrangement, small power supply, electric automobile, demand side response and the like. The load prediction result finished by the model can directly guide the planning work of the regional power distribution network frame, and the degree of accurate load prediction and accurate power grid planning is achieved. The method comprises the following specific steps:
the method comprises the following steps: modeling
The embodiment provides a photovoltaic, wind energy and energy storage source measurement calculation model.
1) Photovoltaic model: the solar power generation power is mainly determined by irradiance and temperature, the output power of the photovoltaic cell assembly at different time periods is approximately in linear relation with environmental factors, and a power estimation formula is as follows:
Figure BDA0002376371710000162
s is actually measured sunlight illumination intensity; t is the actually measured temperature of the battery plate; srefThe illumination intensity under standard conditions is 800W/m2, TrefIs the cell plate temperature under standard conditions; prefThe output power is 3.5kW under the standard condition; and gamma is a preset maximum power temperature coefficient of the photovoltaic equipment.
2) A fan model: the optimum installed capacity of a wind turbine is calculated using the following formula:
Figure BDA0002376371710000171
without considering the capacity of the wind turbine, the hourly power generation load is calculated by:
Pmax＝K×Ca×Ct×S×vt 5×η
the actual input power of the wind turbine is calculated by:
Fw＝min(Ne，Pmax)
in the formula, K is a wind turbine power conversion coefficient, and a default value 0.6127 is taken; ca is an air height density conversion coefficient, which refers to the corrected value of air density at different altitudes and can be found out according to the altitude; ct is an air temperature density correction coefficient, and when the temperature is different, the air density is also different, and the Ct can be found out according to the average temperature; s is the wind sweeping area of the wind turbine blade;
Figure BDA0002376371710000173
is the average wind speed; v. oftFor real time wind speed η is the total efficiency of the wind turbine, a value related to the type of wind turbine, typically 25% to 50%.
3) An energy storage model: the energy storage device stored electricity is an accumulated quantity which changes along with time and is related to the charging and discharging power and the charging and discharging efficiency of the energy storage element, and meanwhile, the self discharging loss is also considered. The mathematical model is as follows:
charging: e (t) ═ (1- τ) E (t-1) + P1(t)η1Δt
Discharging:
Figure BDA0002376371710000172
e (t) is the electric quantity of the energy storage device in a t period; e (t-1) is the electric quantity of the energy storage device in a t-1 time period before the t time period; p1(t) and P2(t) charging power and discharging power, η1The charge-discharge efficiency of the energy storage device is obtained; τ is the self-discharge rate of the energy storage element.
Considering the hardware characteristics of the energy storage device, the charging and discharging power limits are as follows:
0≤P2，P1≤P2
in the formula, P1Representing the minimum value, P, of the charge and discharge power of the energy storage device2Representing the maximum value of the charge and discharge power of the energy storage device;
simultaneously in order to provide the emergent reserve of active power distribution network, the state of charge restraint of setting the energy storage as follows:
E1≤E≤E2
in the formula, E1And E2Indicating the maximum and minimum capacities of the battery.
4) An electric automobile model: the grid electric vehicle charging load may be calculated by:
Fe＝N·q·D1·k1+N·(1-q)·D2·k2
in the formula, FeIs the charging pile load, unit, W; n is the number of the electric parking spaces; q is a slow charge/charge ratio; d1 is a slow pile filling load index; k1 is the slow pile filling coincidence rate; d2 is a quick-filling pile load index; k2 is the rapid pile filling coincidence rate.
5) Demand side load model: firstly, a maximum load prediction model of a single demand side resource in a grid is researched, and calculation is carried out in two situations:
under the condition that the power saving and consumption reduction rate of a certain type of demand side resources of a user is known. The maximum load prediction formula of a certain class of users considering single demand side resources is as follows:
Figure BDA0002376371710000181
in the formula,. DELTA.Pmax，i′Representing the consumption-reducing load in kW under the action of resources on the demand side αEE，i′Representing the consumption reduction rate of the class users under the action of energy efficiency resources, t representing the number of hours, unit and hour in the prediction period βLD，i′Representing the load rate of the class of users under the action of the load class resources; q0，preThe power consumption, unit and kWh of the resources at the demand side are not considered in the forecast year by the users; p0，preThe maximum load, unit, kW of the resource on the demand side is not considered for the users; t ismaxIs the classThe maximum load utilization hours, units, hours of the user.
When the controllable condition of a certain type of demand side resource is known, the calculation formula is as follows:
ΔPmax，i′＝P0，pre·μi′·σi′·ρi′，j
in the formula, P0，preThe maximum load, kW, of the demand side resources is not considered for the class of users; mu.si′As a total controllable load proportion, σi′The demand side resource proportion, ρ, may be called for the time periodi′，jAnd the influence of the demand side response measures on the demand side resources is strengthened.
Under the action of various load resources contained in the grid, the reduced maximum load is as follows:
Figure BDA0002376371710000191
wherein n represents the number of the load resources owned by the user, and ξ is a preset demand-side load coefficient.
Step two: space load calculation
Fig. 2 is a flowchart of a space load calculation method according to an embodiment of the present invention, as shown in fig. 2, the steps are as follows:
1) and (3) distant view prediction:
planning land classification building area or floor area, and calculating the time-by-time load of the grid by adopting the following formula:
Figure BDA0002376371710000192
in the formula: f0' is the grid hourly load;
m is the number of land used in the grid;
Dia unit building/floor area load characteristic curve for the i-th building;
Siis the i-th building/floor space.
2) Prediction of middle year
And predicting the load of the middle year by adopting an S-shaped curve method according to the grid saturation load and the land block development time sequence.
And predicting the load increase condition of each development land in the middle year according to the load increase curve model. The mathematical model of the S-shaped curve method is as follows:
an S-shaped curve mathematical model:
Figure BDA0002376371710000193
wherein:
and Y is the percentage of the annual maximum load to the prospective saturated load in the t year.
t is the number of years from the present year;
a is the growth coefficient of the S-shaped curve;
e is a natural constant.
Step three: load prediction based on active power distribution network
And after the basic space load prediction is completed, an active load model is added to complete more accurate prediction.
Fa＝F0-Fp-Fw+Fe+Fd-Fq
FaFor load prediction results under active distribution networks, F0For space-based legacy loads, FpFor photovoltaic input power, FwFor fan input power, FeFor electric vehicle load, FdFor demand side load, FqPower is input for energy storage.
According to the active power distribution network load prediction method based on the comprehensive energy background, a calculation model of an energy source side is established in the load prediction process, a calculation model of user load measurement is established, load prediction is completed through superposition of two ends, and a basis is provided for network planning.
According to another aspect of the embodiments of the present invention, there is also provided a power grid load prediction apparatus, including: a prediction module and a determination module, which are described in detail below.
The prediction module is used for predicting the load of various load devices in the power grid and the input power of various power supply devices to the power grid, wherein the various loads comprise space loads, electric automobile loads and demand side loads; the multiple input powers comprise photovoltaic input power, fan input power and energy storage input power; and the determining module is connected with the predicting module and used for determining the load predicting result of the power grid according to various loads and various input powers.
Through the device, the space load of the power grid, the load of various load devices in the power grid and the input power of various power supply devices to the power grid are predicted by adopting the prediction module, wherein the various loads comprise at least one of the following loads: electric vehicle load, demand side load; the plurality of input powers includes at least one of: photovoltaic input power, fan input power and energy storage input power; the determining module determines a load prediction result of the power grid based on the space load according to various loads and various input powers, rechecks and predicts the various loads in the power grid and predicts the input powers of various power supply equipment, so that the space load of the power grid predicted by considering more factors is corrected through the various loads and the various power supply equipment, the purpose of accurately determining the load prediction result of the power grid is achieved, the technical effect of improving the accuracy of power grid load prediction is achieved, and the technical problem that the requirements of power grid load prediction cannot be met due to the fact that the factors are less considered in a power grid load prediction method in the related technology is solved.
Optionally, the determining module includes: a determining unit, configured to determine a load prediction result according to a load prediction formula, where the load prediction formula is as follows:
Fa＝F0-Fp-Fw+Fe+Fd-Fq
in the formula, FaLoad prediction results under the active power distribution network are obtained; f0For space load, FpFor photovoltaic input power, FwFor fan input power, FeFor electric vehicle load, FdFor demand side load, FqPower is input for energy storage.
Optionally, the prediction module includes: a first prediction unit for predicting a space load; under the condition of predicting the space load of the distant view year, predicting the space load of the distant view year through a distant view year formula, wherein the distant view year formula is as follows:
Figure BDA0002376371710000211
in the formula, F0' is the prediction result of space load in the distant view year; m is the number of land used in the grid of the space; diA floor area-load characteristic curve for a unit building of class i buildings; siIs the floor space of the i-th building;
the first prediction unit is further configured to, in a case where the space load of the middle year is predicted, predict the space load of the middle year by a middle year formula, wherein the middle year is a year between the present year and the distant view year, and the middle year formula is:
F0″＝Y(t)·F0′
wherein Y is the percentage of the space load of the middle year to the space load of the long-range view year; t is the number of years from the present year;
Figure BDA0002376371710000212
in the formula, A is the growth coefficient of the load in the middle year, and e is a natural constant.
Optionally, the prediction module includes: the second prediction unit is used for predicting the photovoltaic input power under the condition that the photovoltaic input power exists in the power grid; determining the photovoltaic input power according to a photovoltaic power formula, wherein the photovoltaic power formula is as follows:
Figure BDA0002376371710000213
in the formula, FpThe photovoltaic input power is obtained, and S is actually measured solar illumination intensity; t is the actually measured temperature of the battery plate; srefIs the intensity of light under standard conditions, PrefIs the output power under standard conditions; t isrefIs the cell plate temperature under standard conditions; and gamma is a preset maximum power temperature coefficient of the photovoltaic equipment.
Optionally, the prediction module includes: the third prediction unit is used for predicting the input power of the fan under the condition that the input power of the fan exists in the power grid; determining the input power of the fan according to a fan power formula, wherein the fan power formula is as follows:
Fw＝min(Ne，Pmax)
in the formula, NeFor optimum installed capacity of the fan, PmaxThe power generation load without regard to the capacity of the wind turbine;
Figure BDA0002376371710000221
in the formula, K is a wind turbine power conversion coefficient; caIs the air height density conversion coefficient; ctIs the air temperature density correction coefficient; s is the wind sweeping area of the wind turbine blade;
Figure BDA0002376371710000222
to predict annual average wind speed, η for full fan efficiency,
Pmax＝K×Ca×Ct×S×vt 3×η
in the formula, vtIs the real-time wind speed.
Optionally, the prediction module includes: the fourth prediction unit is used for predicting the electric vehicle load under the condition that the electric vehicle load exists in the power grid;
the fourth prediction unit is used for determining the electric automobile load according to an electric automobile load formula, wherein the electric automobile load formula is as follows:
Fe＝N·q·D1·k1+N·(1-q)·D2·k2
in the formula, FeIs the electric automobile load; n is electric automobileA number of bits; q is a slow charge/charge ratio; d1The load index of the slow filling pile is obtained; k is a radical of1The pile slow-filling synchronization rate is obtained; d2The load index of the quick filling pile is obtained; k is a radical of2The pile filling is performed quickly.
Optionally, the prediction module includes: the fifth prediction unit is used for predicting the demand side load under the condition that the demand side load exists in the power grid;
the fifth prediction unit is used for determining a first single demand side load according to a first single demand side load formula under the condition of determining the consumption reduction rate of the i' class demand side resource of the user, wherein the first single demand side load formula is as follows:
Figure BDA0002376371710000223
in the formula,. DELTA.Pmax，i′Representing consumption reduction load under the action of i' type demand side resources αEE，i′Representing consumption reduction rate of resources on the i' class demand side of the user, t representing hours in the prediction period, βLD，i′Representing the load rate of the user under the action of the load-class resources; q0，preThe power consumption of the resources on the demand side is not considered by the user in the forecast year;
Q0，pre＝P0，pre·Tmax
in the formula, P0，preThe maximum load of the demand side resource is not considered for the user; t ismaxNumber of hours utilized for maximum load of user:
the fifth prediction unit is further configured to determine, according to a second single demand-side load formula, a second single demand-side load of the i 'class of demand-side resources under a condition that the i' class of demand-side resources of the user is controllable, where the second single demand-side load formula is:
ΔPmax，i′＝P0，pre·μi′·σi′·ρi′，j
in the formula, mui′For the total controllable load proportion, σ, of the i' class demand side resourcesi′Adjustable demand side resource proportion, ρ, for i' type demand side resourcesi′，jThe influence coefficient of the response measure j on the i' type demand side resource; determining a demand side load according to a demand side load formula, wherein the demand side load formula is as follows:
Figure BDA0002376371710000231
where n represents the number of demand side resources owned by the user and ξ is the preset demand side load factor.
Optionally, the prediction module includes: the sixth prediction unit is used for predicting the energy storage input power under the condition that the energy storage input power exists in the power grid;
the sixth prediction unit is used for predicting the energy storage input power according to a charging energy storage input power formula under the condition that the energy storage device is charged, wherein the charging energy storage input power formula is as follows:
Fq＝-P1(t)
in the formula, P1(t) is the charging power of the energy storage device; p1(t) satisfies the following formula:
E(t)＝(1-τ)E(t-1)+P1(t)η1Δt
wherein E (t) is the electric quantity of the energy storage device in the t period, E (t-1) is the electric quantity of the energy storage device in the t-1 period before the t period, η1The charge-discharge efficiency of the energy storage device is obtained; τ is the self-discharge rate of the energy storage device;
the sixth prediction unit is further configured to predict the energy storage input power according to a discharge energy storage input power formula under a condition that the energy storage device is discharged, where the discharge energy storage input power formula is:
Fq＝P2(t)
in the formula, P2(t) is the discharge power of the energy storage device; p2(t) satisfies the following formula:
Figure BDA0002376371710000232
according to another aspect of the embodiments of the present invention, there is also provided a storage medium, where the storage medium includes a stored program, and when the program runs, the apparatus on which the storage medium is located is controlled to execute the power grid load prediction method according to any one of the above.
According to another aspect of the embodiments of the present invention, there is also provided a processor, configured to execute a program, where the program executes to perform the grid load prediction method of any one of the above.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, which can store program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (10)

1. A power grid load prediction method is characterized by comprising the following steps:
predicting the space load of the power grid, and the load of various load devices and the input power of various power supply devices in the power grid to the power grid, wherein the various loads comprise at least one of the following: electric vehicle load, demand side load; the plurality of input powers includes at least one of: photovoltaic input power, fan input power and energy storage input power;
and determining a load prediction result of the power grid based on the space load according to the various loads and the various input powers.
2. The method of claim 1, wherein determining a load prediction for the grid based on the space load from the plurality of loads and the plurality of input powers comprises: determining the load prediction result according to a load prediction formula, wherein the load prediction formula is as follows:
Fa＝F0-Fp-Fw+Fe+Fd-Fq
in the formula, FaLoad prediction results under the active power distribution network are obtained; f0For space load, FpFor photovoltaic input power, FwFor fan input power, FeFor electric vehicle load, FdFor demand side load, FgPower is input for energy storage.
3. The method of claim 2, wherein predicting a plurality of loads in the grid comprises: predicting the space load;
in the case of predicting the space load of a distant view year, predicting the space load of the distant view year by a distant view year formula, wherein the distant view year formula is as follows:
Figure FDA0002376371700000011
in the formula, F0' is the prediction result of the space load in the distant view year; m is the number of land used in the grid of the space; diA floor area-load characteristic curve for a unit building of class i buildings; siIs the floor space of the i-th building;
alternatively, the first and second electrodes may be,
in the case of predicting the space load of an intermediate year, the space load of the intermediate year is predicted by an intermediate year formula, wherein the intermediate year is a year between a current year and a distant view year, and the intermediate year formula is as follows:
F0″＝Y(t)·F0′
wherein Y is the percentage of the space load of the middle year to the space load of the long-range view year; t is the number of years from the present year;
Figure FDA0002376371700000021
in the formula, A is the growth coefficient of the load in the middle year, and e is a natural constant.
4. The method of claim 3, wherein predicting a plurality of loads on the grid further comprises: predicting a photovoltaic input power in the presence of the photovoltaic input power in the grid;
determining the photovoltaic input power according to a photovoltaic power formula, wherein the photovoltaic power formula is as follows:
Figure FDA0002376371700000022
in the formula, FpThe photovoltaic input power is obtained, and S is actually measured solar illumination intensity; t is the actually measured temperature of the battery plate; srefIs the intensity of light under standard conditions, PrefIs the output power under standard conditions; t isrefIs the cell plate temperature under standard conditions; and gamma is a preset maximum power temperature coefficient of the photovoltaic equipment.
5. The method of claim 3, wherein predicting a plurality of loads on the grid further comprises: predicting the input power of the fan under the condition that the input power of the fan exists in the power grid;
determining the fan input power according to a fan power formula, wherein the fan power formula is as follows:
Fw＝min(Ne，Pmax)
in the formula, NeFor optimum installed capacity of the fan, PmaxThe power generation load without regard to the capacity of the wind turbine;
Figure FDA0002376371700000023
in the formula, K is a wind turbine power conversion coefficient; caIs the air height density conversion coefficient; ctFor air temperature sealingA degree correction coefficient; s is the wind sweeping area of the wind turbine blade;
Figure FDA0002376371700000024
to predict annual average wind speed, η for full fan efficiency,
Pmax＝K×Ca×Ct×S×vt 3×η
in the formula, vtIs the real-time wind speed.
6. The method of claim 3, wherein predicting a plurality of loads on the grid further comprises: predicting an electric vehicle load in the presence of the electric vehicle load in the grid;
determining the electric vehicle load according to an electric vehicle load formula, wherein the electric vehicle load formula is as follows:
Fe＝N·q·D1·k1+N·(1-q)·D2·k2
in the formula, FeIs the electric automobile load; n is the number of the electric automobile parking spaces; q is a slow charge/charge ratio; d1The load index of the slow filling pile is obtained; k is a radical of1The pile slow-filling synchronization rate is obtained; d2The load index of the quick filling pile is obtained; k is a radical of2The pile filling is performed quickly.
7. The method of claim 3, wherein predicting a plurality of loads in the grid comprises: predicting a demand side load when the demand side load is present in the grid;
under the condition of determining the consumption reduction rate of i' class demand side resources of a user, determining a first single demand side load according to a first single demand side load formula, wherein the first single demand side load formula is as follows:
Figure FDA0002376371700000031
in the formula,. DELTA.Pmax，i′Representing consumption reduction load under the action of i' type demand side resources αEE，i′Representing consumption reduction rate of resources on the i' class demand side of the user, t representing hours in the prediction period, βLD，i′Representing the load rate of the user under the action of the load-class resources; q0，preRepresenting that the user does not consider the electricity consumption of the resource at the demand side in the forecast year;
Q0，pre＝P0，pre·Tmax
in the formula, P0，preNot considering the maximum load of demand side resources for the user; t ismaxA number of hours of maximum load utilization for the user;
alternatively, the first and second electrodes may be,
under the condition that the i 'type demand side resources of the user can be regulated, determining a second single demand side load of the i' type demand side resources according to a second single demand side load formula, wherein the second single demand side load formula is as follows:
ΔPmax，i＇＝P0，pre·μi＇·σi＇·ρi′，j
in the formula, mui′For the total controllable load proportion, σ, of the i' class demand side resourcesi＇Adjustable demand side resource proportion, ρ, for i' type demand side resourcesi′，jThe influence coefficient of the response measure j on the i' type demand side resource;
determining the demand side load according to a demand side load formula, wherein the demand side load formula is as follows:
Figure FDA0002376371700000041
where n represents the number of demand side resources owned by the user and ξ is the preset demand side load factor.
8. The method of claim 3, wherein predicting a plurality of loads in the grid comprises: predicting an energy storage input power when the energy storage input power exists in the power grid;
under the condition that the energy storage equipment is charged, predicting the energy storage input power according to a charging energy storage input power formula, wherein the charging energy storage input power formula is as follows:
Fq＝-P1(t)
in the formula, P1(t) is the charging power of the energy storage device; p1(t) satisfies the following formula:
E(t)＝(1-τ)E(t-1)+P1(t)η1Δt
wherein E (t) is the electric quantity of the energy storage device in a t period, E (t-1) is the electric quantity of the energy storage device in a t-1 period before the t period, η1The charge-discharge efficiency of the energy storage device; τ is the self-discharge rate of the energy storage device;
under the condition that the energy storage equipment is discharged, predicting the energy storage input power according to a discharge energy storage input power formula, wherein the discharge energy storage input power formula is as follows:
Fq＝P2(t)
in the formula, P2(t) is the discharge power of the energy storage device; p2(t) satisfies the following formula:
Figure FDA0002376371700000042
9. a grid load prediction device, comprising:
the prediction module is used for predicting the space load of the power grid, the load of various load devices in the power grid and the input power of various power supply devices to the power grid, wherein the various loads comprise at least one of the following: electric vehicle load, demand side load; the plurality of input powers includes at least one of: photovoltaic input power, fan input power and energy storage input power;
and the determining module is used for determining a load prediction result of the power grid based on the space load according to the various loads and the various input powers.
10. A processor configured to execute a program, wherein the program executes to perform the grid load prediction method according to any one of claims 1 to 8.
CN202010067364.7A 2020-01-20 2020-01-20 Power grid load prediction method and device Pending CN111130107A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113112090A (en) * 2021-04-29 2021-07-13 内蒙古电力（集团）有限责任公司内蒙古电力经济技术研究院分公司 Space load prediction method based on principal component analysis of comprehensive mutual information degree

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113112090A (en) * 2021-04-29 2021-07-13 内蒙古电力（集团）有限责任公司内蒙古电力经济技术研究院分公司 Space load prediction method based on principal component analysis of comprehensive mutual information degree
CN113112090B (en) * 2021-04-29 2023-12-19 内蒙古电力（集团）有限责任公司内蒙古电力经济技术研究院分公司 Space load prediction method based on principal component analysis of comprehensive mutual informativity

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