CN114938015A - Energy storage control method and system considering new energy consumption - Google Patents

Energy storage control method and system considering new energy consumption Download PDF

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CN114938015A
CN114938015A CN202210609634.1A CN202210609634A CN114938015A CN 114938015 A CN114938015 A CN 114938015A CN 202210609634 A CN202210609634 A CN 202210609634A CN 114938015 A CN114938015 A CN 114938015A
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energy storage
new energy
value
energy
power
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高潮
田立亭
周渊
程林
林恩德
田文辉
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Tsinghua University
China Three Gorges Corp
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Tsinghua University
China Three Gorges Corp
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention provides an energy storage control method and system considering new energy consumption, wherein the method comprises the following steps: acquiring a new energy power predicted value at the next moment and a new energy load power predicted value at the next moment; constructing an energy storage optimization control model by using a maximum new energy consumption target function within preset time based on a new energy power predicted value at the next moment and a new energy load power predicted value at the next moment; and issuing an output instruction value to the energy storage battery based on the solution value of the energy storage optimization control model. By predicting the short-term new energy power and load power, establishing an energy storage optimization model by taking the minimum new energy abandon as an optimization target, solving the model, achieving the optimal control effect and effectively reducing the new energy abandon rate.

Description

Energy storage control method and system considering new energy consumption
Technical Field
The invention relates to the technical field of energy storage control, in particular to an energy storage control method and system considering new energy consumption.
Background
The power grid load presents the characteristic of low valley at double peaks and night in the daytime due to the habit of human activities, and in the load valley period, the power system power imbalance is caused by the fact that too much wind power and photoelectric electricity are accessed, or the thermal power generating unit runs at low output for a long time and is frequently started and stopped, so that the running economy is poor, and the phenomenon that a large amount of wind and light are abandoned occurs. The existing energy storage control technology is optimized by taking the economy of the integral system of the regional energy storage and power generation as a realization target, and the improvement on the new energy abandon phenomenon is limited.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defect that the existing energy storage control technology in the prior art has limited improvement on the new energy discarding phenomenon, so as to provide an energy storage control method and system considering new energy consumption.
The technical scheme provided by the invention is as follows:
in a first aspect, an embodiment of the present invention provides an energy storage control method considering new energy consumption, including:
acquiring a new energy power predicted value at the next moment and a new energy load power predicted value at the next moment;
constructing an energy storage optimization control model by using a maximum new energy consumption target function within preset time based on a new energy power predicted value at the next moment and a new energy load power predicted value at the next moment;
and issuing an output instruction value to the energy storage battery based on the solution value of the energy storage optimization control model.
Optionally, the energy storage control method considering new energy consumption further includes:
and when the energy storage optimization control model has no solution value, the power shortage is averagely distributed according to the number of the energy storage batteries and is issued as the output instruction value of the energy storage batteries.
Alternatively, the objective function is represented as follows:
Figure BDA0003671541760000021
wherein, P pv-predict And P load-predict Respectively photovoltaic power predicted value and load power predicted value, SOE i And SOE j Energy storage cell energy states, P, of energy storage cell i and energy storage cell j, respectively i And P j Average power, omega, of energy storage battery i and energy storage battery j in an optimization period respectively 1 And ω 2 And respectively reducing respective influence weight coefficients of new energy abandon and energy storage battery SOE balance in the objective function, wherein n is the number of the energy storage batteries, and T is an optimization period.
Optionally, the energy storage optimization control model has the following constraints:
Figure BDA0003671541760000031
wherein, P imin Optimizing the minimum value of the average power in the period, P, for the energy storage cell i imax Optimizing the average Power maximum, SOE, over a period for an energy storage Battery i imin For the minimum value of the energy state of the energy storage battery i, SOE imax And taking the maximum value of the energy state of the energy storage battery i.
Optionally, the obtaining the predicted value of the new energy power at the next moment and the predicted value of the new energy load power at the next moment includes:
acquiring a measured value of the new energy power and the load power at the current moment and a predicted value of the new energy power and the load power at the current moment from the previous moment;
and correcting errors by a rolling optimization method, and predicting the new energy power and the load power at the next moment.
Optionally, the energy storage control method considering new energy consumption further includes: and returning to the step of obtaining the new energy power predicted value and the new energy load power predicted value after the step of issuing the output instruction value to the energy storage battery based on the solution value of the energy storage optimization control model is executed.
Optionally, when the energy storage optimization control model has no solution value, the calculation expression of the output instruction value of the energy storage battery is as follows:
P i ＝(P load-predict -P pv-predict )/n。
in a second aspect, an embodiment of the present invention provides an energy storage control system for accounting new energy consumption, including:
the rolling prediction module is used for obtaining a new energy power prediction value and a new energy load power prediction value;
the model construction module is used for constructing an energy storage optimization control model by using a maximum new energy consumption target function within preset time based on the new energy power predicted value and the new energy load power predicted value;
and the instruction issuing module is used for issuing an output instruction value to the energy storage battery based on the solution value of the energy storage optimization control model.
In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to cause the computer to execute the energy storage control method that accounts for new energy consumption according to the first aspect of the embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention provides a computer device, including: the energy storage control method comprises a memory and a processor, wherein the memory and the processor are communicatively connected with each other, the memory stores computer instructions, and the processor executes the computer instructions so as to execute the energy storage control method taking account of new energy consumption according to the first aspect of the embodiment of the invention.
The technical scheme of the invention has the following advantages:
the invention provides an energy storage control method considering new energy consumption, which comprises the following steps: acquiring a new energy power predicted value at the next moment and a new energy load power predicted value at the next moment; constructing an energy storage optimization control model by using a maximum new energy consumption target function within preset time based on a new energy power predicted value at the next moment and a new energy load power predicted value at the next moment; and issuing an output instruction value to the energy storage battery based on the solution value of the energy storage optimization control model. By predicting the short-term new energy power and load power, establishing an energy storage optimization model by taking the minimum new energy abandon as an optimization target, solving the model, achieving the optimal control effect and effectively reducing the new energy abandon rate.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a specific example of an energy storage control method in consideration of new energy consumption according to an embodiment of the present invention;
FIG. 2 is a graph of photovoltaic and load power variation in an embodiment of the present invention;
FIG. 3 is a functional block diagram of one particular example of an energy storage control system that accounts for new energy consumption in an embodiment of the present invention;
fig. 4 is a block diagram of a specific example of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood 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.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Furthermore, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
An embodiment of the present invention provides an energy storage control method considering new energy consumption, as shown in fig. 1, including the following steps:
step S1: and acquiring a new energy power predicted value at the next moment and a new energy load power predicted value at the next moment.
In a specific embodiment, the new energy power predicted value at the next moment and the new energy load power predicted value at the next moment are obtained through the following steps:
step S11: and acquiring the measured values of the new energy power and the load power at the current moment and the predicted values of the new energy power and the load power at the current moment from the previous moment.
Step S12: and correcting errors by a rolling optimization method, and predicting the new energy power and the load power at the next moment.
In the embodiment of the invention, for the energy storage control process for a period of time, in the optimization periodWhen the time is small enough, the new energy power and load power change curve can be assumed to be composed of a plurality of broken lines, taking photovoltaic power prediction as an example, and the photovoltaic power measured value at the time t is assumed to be P pv-t The predicted value of the photovoltaic power at the moment of t +1 is P pv-t+T-predict And the measured value of the photovoltaic power at the T + T moment is P pv-T+1 Then the prediction error Δ P pv ＝P pv-t+T -P pv-t+T-predict The actual change being dP pv ＝P pv-t+T -P pv-t Then, the photovoltaic power value at the time T +2T can be predicted as follows:
P pv-t+2T-predict ＝P pv-t+T +dP pv +ΔP pv 。
the prediction principle of the load power is the same as that of the photovoltaic power prediction, and the details are not repeated herein.
Compared with the existing energy storage control method, the method has the advantages that the rolling prediction of the new energy power and the load power is added, the latest measured data is fully utilized, and the control accuracy is improved. Meanwhile, the new energy power generation power and the load power are predicted by adopting a rolling optimization method, the requirements on historical data are low, and the method is suitable for a rapid deployment scene of a new energy power station.
Step S2: and constructing an energy storage optimization control model by using the maximum new energy consumption in preset time as an objective function based on the predicted new energy power value at the next moment and the predicted new energy load power value at the next moment.
In a specific embodiment, the description of the energy storage optimization control model is divided into three parts, namely an objective function, a constraint condition and an optimization variable.
Therein, the objective function is described as follows: the objective function is solved according to the invention, the maximum new energy consumption in a time interval is taken as the objective function, and the expression is as follows:
Figure BDA0003671541760000081
wherein, P pv-predict And P load-predict Respectively photovoltaic power predicted value and load power predicted value, SOE i And SOE j The Energy states (State of Energy, short for SOE), P of the Energy storage battery i and the Energy storage battery j respectively i And P j Average power, omega, of energy storage battery i and energy storage battery j in an optimization period respectively 1 And ω 2 And respectively reducing respective influence weight coefficients of new energy abandon and energy storage battery SOE balance in the objective function, wherein n is the number of the energy storage batteries, and T is an optimization period.
Further, the constraint is described as follows: and (4) listing constraint conditions for the optimization object according to known conditions and physical constraints, wherein the constraint conditions comprise the SOE limit of the energy storage battery, the output power limit of the energy storage battery and the like. The expression is as follows:
Figure BDA0003671541760000082
wherein, P imin Optimizing the minimum value of the average power in the period, P, for the energy storage cell i imax Optimizing the maximum value of the average power, SOE, over a period for an energy storage cell i imin For the minimum value of the energy state of the energy storage battery i, SOE imax And taking the maximum value of the energy state of the energy storage battery i.
Further, the optimization variables are described as follows: the optimized variable of the embodiment is the output P of the energy storage battery i 。
Step S3: and issuing an output instruction value to the energy storage battery based on the solution value of the energy storage optimization control model.
In a specific embodiment, when the optimization problem has a solution, the corresponding solution value is issued to the energy storage battery as the output instruction value of the energy storage battery. When the optimization problem is not solved, namely the energy storage optimization control model is not solved, the power shortage is evenly distributed according to the number of the energy storage batteries and is issued as the output instruction value of the energy storage batteries. The calculation expression of the output of the energy storage battery without solution is as follows:
P i ＝(P load-predict -P pv-predict )/n。
in an embodiment, the energy storage control method considering new energy consumption further includes: and returning to the step of obtaining the new energy power predicted value and the new energy load power predicted value after the step of issuing the output instruction value to the energy storage battery based on the solution value of the energy storage optimization control model is executed.
In a specific embodiment, after the step of issuing the output instruction value to the energy storage battery based on the solution value of the energy storage optimization control model is executed, it represents that 1 optimization cycle is ended. When 1 optimization cycle is finished, the next optimization cycle is entered, and the process goes to step S1.
The invention provides an energy storage control method considering new energy consumption, which comprises the following steps: acquiring a new energy power predicted value at the next moment and a new energy load power predicted value at the next moment; constructing an energy storage optimization control model by taking the maximum new energy consumption in preset time as an objective function based on the predicted new energy power value at the next moment and the predicted new energy load power value at the next moment; and issuing an output instruction value to the energy storage battery based on the solution value of the energy storage optimization control model. By predicting the short-term new energy power and load power, establishing an energy storage optimization model by taking the minimum new energy abandon as an optimization target, solving the model, achieving the optimal control effect and effectively reducing the new energy abandon rate. And simultaneously, the SOE between the energy storage batteries can be converged, the charging and discharging times of the energy storage batteries can be reduced, and the service life of the energy storage batteries can be prolonged.
In one embodiment, the case model consists of 2 energy storage cells and 1 photovoltaic power plant.
The present embodiment comprises two main steps: and (4) power prediction and optimization solution.
The method comprises the following steps: and (4) power prediction. In the embodiment, the photovoltaic power and load prediction is performed by using a rolling optimization method, and for a period of time of the energy storage control process, when the optimization period is sufficiently small (in the case of the present invention, 2 seconds), the photovoltaic and load power change curve may be assumed to be composed of a plurality of broken lines, as shown in fig. 2.
Setting the photovoltaic power measured value to be P at the moment t pv-t The predicted value of the photovoltaic power at the moment of t +1 is P p-t+T-predict And the measured value of the photovoltaic power at the moment T + T is P pv-T+1 Then the prediction error Δ P pv ＝P pv-t+T -P pv-t+T-predict The actual change is dP pv ＝P pv-t+T -P pv-t Then, the photovoltaic power value at the time T +2T can be predicted as follows:
P pv-t+2T-predict ＝P pv-t+T +dP pv +ΔP pv 。
the prediction principle of the load power is the same as that of the photovoltaic power prediction, and is not described in detail herein.
Step two: and (3) optimizing and solving:
step two, zero and one: and describing the optimization model, wherein the description of the optimization model is divided into three parts, namely an objective function, a constraint condition and an optimization variable.
1) Describing the objective function, in this case, the predicted value of the photovoltaic power generation power is set as P pv-predict The predicted value of the load power is P load-predict The output instruction of the energy storage battery 1 is P 1 The output instruction of the energy storage battery 2 is P 2 The SOE of the energy storage battery 1 is SOE 1 The SOE of the energy storage battery 2 is SOE 2 The optimization period is 2 s.
Setting the SOE of the energy storage battery at the t moment as the SOE t And the output of the energy storage battery in the time period from T to T + T is P, so the SOE of the energy storage battery at the time of T + T is as follows:
Figure BDA0003671541760000111
weighting omega for reducing new energy abandonment and energy storage battery SOE balance influence 1 And ω 2 Respectively 0.8 and 0.2, and the optimization period T is 2s, the objective function can be expressed as:
Figure BDA0003671541760000112
2) constraint conditions are described, and the constraint conditions of the embodiment comprise energy storage battery output constraint and energy storage battery SOE constraint. The constraint conditional expression in this example is as follows:
Figure BDA0003671541760000121
3) describing an optimized variable, wherein the optimized variable obtained through optimization in the case is the output instruction P of the energy storage battery 1 1 The output instruction of the energy storage battery 2 is P 2 。
Step two, zero two: and issuing an energy storage battery output instruction. And issuing instruction values to the energy storage battery according to different strategies according to whether the optimization problem has a solution.
1) And when the optimization problem is solved, issuing a corresponding solution value to the energy storage battery to be used as an output instruction value of the energy storage battery.
2) And when the optimization problem is not solved, the power shortage is averagely distributed according to the number of the energy storage batteries and is issued as the output instruction value of the energy storage batteries. The calculation expression is:
P 1 ＝P 2 ＝(P load-predict -P pv-predict )/2
and at this point, finishing 1 optimization cycle, and turning to the first step to predict the power when the next optimization cycle starts.
An embodiment of the present invention further provides an energy storage control system considering new energy consumption, as shown in fig. 3, including:
and the rolling prediction module 1 is used for obtaining a new energy power prediction value and a new energy load power prediction value. For details, refer to the related description of step S1 in the above embodiment, and are not repeated herein.
And the model construction module 2 is used for constructing an energy storage optimization control model by using a maximum new energy consumption in a preset time as an objective function based on the new energy power predicted value and the new energy load power predicted value. For details, refer to the related description of step S2 in the above embodiment, and are not described herein again.
And the instruction issuing module 3 is used for issuing an output instruction value to the energy storage battery based on the solution value of the energy storage optimization control model. For details, refer to the related description of step S3 in the above embodiment, and are not described herein again.
An embodiment of the present invention further provides a computer device, as shown in fig. 4, the device terminal may include a processor 61 and a memory 62, where the processor 61 and the memory 62 may be connected by a bus or in another manner, and fig. 4 takes the connection by the bus as an example.
The processor 61 may be a Central Processing Unit (CPU). The Processor 61 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 62, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as the corresponding program instructions/modules in embodiments of the present invention. The processor 61 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 62, that is, the energy storage control method in the above method embodiment, which takes the new energy consumption into account.
The memory 62 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 61, and the like. Further, the memory 62 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 62 may optionally include memory located remotely from the processor 61, and these remote memories may be connected to the processor 61 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more modules are stored in memory 62 and, when executed by processor 61, perform the energy storage control method of embodiments that accounts for new energy consumption.
The details of the computer device may be understood by referring to the corresponding related descriptions and effects in the embodiments, and are not described herein again.
Those skilled in the art will appreciate that all or part of the processes in the methods of the embodiments described above can be implemented by hardware instructed by a computer program, and the program can be stored in a computer readable storage medium, and when executed, the program can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.

Claims (10)

1. An energy storage control method considering new energy consumption is characterized by comprising the following steps:
acquiring a new energy power predicted value at the next moment and a new energy load power predicted value at the next moment;
constructing an energy storage optimization control model by using a maximum new energy consumption target function within preset time based on a new energy power predicted value at the next moment and a new energy load power predicted value at the next moment;
and issuing an output instruction value to the energy storage battery based on the solution value of the energy storage optimization control model.
2. The energy storage control method in consideration of new energy consumption according to claim 1, further comprising:
and when the energy storage optimization control model has no solution value, the power shortage is evenly distributed according to the number of the energy storage batteries and is issued as an output instruction value of the energy storage batteries.
3. The method of energy storage control taking into account new energy consumption of claim 1, wherein the objective function is expressed as follows:
Figure FDA0003671541750000011
wherein, P pv-predict And P load-predict Respectively photovoltaic power predicted value and load power predicted value, SOE i And SOE j Energy storage cell energy states, P, of energy storage cell i and energy storage cell j, respectively i And P j Average power, omega, of energy storage battery i and energy storage battery j in an optimization period respectively 1 And ω 2 And respectively reducing respective influence weight coefficients of new energy abandon and energy storage battery SOE balance in the objective function, wherein n is the number of the energy storage batteries, and T is an optimization period.
4. The energy storage control method considering new energy consumption according to claim 3, wherein the energy storage optimization control model has the following constraints:
Figure FDA0003671541750000021
wherein, P imin Optimizing the minimum value of the average power in the period, P, for the energy storage cell i imax Optimizing the maximum value of the average power, SOE, over a period for an energy storage cell i imin For the minimum value of the energy state of the energy storage battery i, SOE imax The energy state of the energy storage battery i is the maximum value.
5. The energy storage control method for calculating new energy consumption according to claim 1, wherein the obtaining of the new energy power predicted value at the next moment and the new energy load power predicted value at the next moment comprises:
acquiring a measured value of the new energy power and the load power at the current moment and a predicted value of the new energy power and the load power at the current moment from the previous moment;
and correcting errors by a rolling optimization method, and predicting the new energy power and the load power at the next moment.
6. The energy storage control method in consideration of new energy consumption according to claim 1, further comprising: and returning to the step of obtaining the new energy power predicted value and the new energy load power predicted value after the step of sending the output instruction value to the energy storage battery based on the solution value of the energy storage optimization control model is executed.
7. The energy storage control method considering new energy consumption according to claim 2, wherein when the energy storage optimization control model has no solution value, the calculation expression of the output instruction value of the energy storage battery is as follows:
P i ＝(P load-predict -P pv-predict )/n。
8. an energy storage control system that accounts for new energy consumption, comprising:
the rolling prediction module is used for obtaining a new energy power prediction value and a new energy load power prediction value;
the model construction module is used for constructing an energy storage optimization control model by using a maximum new energy consumption target function within preset time based on the new energy power predicted value and the new energy load power predicted value;
and the instruction issuing module is used for issuing an output instruction value to the energy storage battery based on the solution value of the energy storage optimization control model.
9. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of energy storage control in consideration of new energy consumption according to any one of claims 1-7.
10. A computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the energy storage control method according to any one of claims 1 to 7, wherein the method takes into account new energy consumption.
CN202210609634.1A 2022-05-31 2022-05-31 Energy storage control method and system considering new energy consumption Pending CN114938015A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115659595A (en) * 2022-09-26 2023-01-31 中国华能集团清洁能源技术研究院有限公司 Energy storage control method and device of new energy station based on artificial intelligence

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115659595A (en) * 2022-09-26 2023-01-31 中国华能集团清洁能源技术研究院有限公司 Energy storage control method and device of new energy station based on artificial intelligence
CN115659595B (en) * 2022-09-26 2024-02-06 中国华能集团清洁能源技术研究院有限公司 Energy storage control method and device for new energy station based on artificial intelligence

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