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

Abstract

The invention provides an energy storage control method and system considering new energy consumption. The method includes: obtaining a new energy power prediction value at the next moment and a new energy load power prediction value at the next moment; The power prediction value and the new energy load power prediction value at the next moment are used to construct an energy storage optimization control model with the maximum new energy consumption in the preset time as the objective function; based on the solution value of the energy storage optimization control model, the energy storage battery is issued Output command value. By predicting the short-term new energy power and load power, an energy storage optimization model is established with the minimum new energy disposal as the optimization goal, and the model is solved to achieve the optimal control effect and effectively reduce the new energy disposal rate.

Description

An 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 that takes into account new energy consumption.

Background technique

Due to human activity habits, the power grid load has the characteristics of double peaks during the day and troughs at night. During the load trough period, connecting too much wind power and photovoltaics will cause power imbalance in the power system or long-term low-output operation of thermal power units and frequent start and stop, and the operation economy will change. Poor, so there is a large number of abandoned wind and light phenomenon. Most of the existing energy storage control technologies are optimized for the economy of the overall system of regional energy storage and power generation, and the improvement of the new energy disposal phenomenon is limited.

SUMMARY OF THE INVENTION

Therefore, the technical problem to be solved by the present invention is to overcome the defect of the existing energy storage control technology in the prior art that the improvement of the new energy disposal phenomenon is limited, so as to provide an energy storage control method and system that takes into account the consumption of new energy .

The technical scheme proposed by the present invention is as follows:

In a first aspect, an embodiment of the present invention provides an energy storage control method that takes into account new energy consumption, including:

Obtain the predicted value of new energy power at the next moment and the predicted value of new energy load power at the next moment;

Based on 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, the energy storage optimization control model is constructed with the maximum new energy consumption in the preset time as the objective function;

Based on the solution value of the energy storage optimization control model, a force command value is sent to the energy storage battery.

Optionally, the energy storage control method considering new energy consumption further includes:

When the energy storage optimization control model has no solution value, the power shortage is evenly distributed according to the number of energy storage batteries, and issued as the output command value of the energy storage batteries.

Optionally, the objective function is expressed as follows:

Among them, P _pv-predict and P _load-predict are the photovoltaic power prediction value and load power prediction value, respectively, SOE _i and SOE _j are the energy storage battery energy states of energy storage battery i and energy storage battery j, respectively, P _i and P _j is the average power of energy storage battery i and energy storage battery j in the optimization period, respectively, ω ₁ and ω ₂ are the respective influence weight coefficients of reducing new energy abandonment and energy storage battery SOE balance in the objective function, n is the number of energy storage batteries, and T is the optimization period.

Optionally, the energy storage optimization control model constraints are as follows:

Among them, P _imin is the minimum value of the average power of the energy storage battery i in the optimization period, P _imax is the maximum value of the average power in the optimization period of the energy storage battery i, and SOE _imin is the minimum value of the energy state of the energy storage battery i of the energy storage battery , SOE _imax is the maximum value of the energy state of the energy storage battery i.

Optionally, obtaining the predicted value of the new energy power at the next moment and the predicted value of the new energy load at the next moment includes:

Obtain the measured value of the new energy power and load power at the current moment and the predicted value of the new energy power and load power at the current moment at the previous moment;

The error is corrected by the method of rolling optimization, and the new energy power and load power are predicted at the next moment.

Optionally, the energy storage control method considering new energy consumption further includes: after executing the step of issuing a force command value to the energy storage battery based on the solution value of the energy storage optimization control model, returning to obtain new energy Steps of power forecast value and new energy load power forecast value.

Optionally, when the energy storage optimization control model has no solution value, the calculation expression of the output command value of the energy storage battery is:

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 that takes into account new energy consumption, including:

The rolling forecast module is used to obtain the new energy power forecast value and the new energy load power forecast value;

a model building module, configured to construct an energy storage optimization control model based on the new energy power prediction value and the new energy load power prediction value, taking the maximum new energy consumption within a preset time as the objective function;

The command issuing module is configured to issue a force command 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 the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the first aspect of the embodiment of the present invention. Energy storage control method considering new energy consumption.

In a fourth aspect, an embodiment of the present invention provides a computer device, including: a memory and a processor, the memory and the processor are connected in communication with each other, the memory stores computer instructions, and the processor executes the The computer instruction is executed, thereby executing the energy storage control method considering the consumption of new energy according to the first aspect of the embodiment of the present invention.

The technical scheme of the present invention has the following advantages:

The invention provides an energy storage control method that takes into account new energy consumption, comprising: obtaining a new energy power prediction value at the next moment and a new energy load power prediction value at the next moment; Based on the predicted value of the new energy load power at the next moment, the energy storage optimization control model is constructed with the maximum new energy consumption in the preset time as the objective function; based on the solution value of the energy storage optimization control model, the force command value is sent to the energy storage battery. By predicting the short-term new energy power and load power, an energy storage optimization model is established with the minimum new energy disposal as the optimization goal, and the model is solved to achieve the optimal control effect and effectively reduce the new energy disposal rate.

Description of drawings

In order to illustrate the specific embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description The drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

FIG. 1 is a flowchart of a specific example of an energy storage control method considering new energy consumption in an embodiment of the present invention;

Fig. 2 is the variation curve of photovoltaic and load power in the embodiment of the present invention;

3 is a schematic block diagram of a specific example of an energy storage control system that takes into account new energy consumption in an embodiment of the present invention;

FIG. 4 is a composition diagram of a specific example of a computer device provided by an embodiment of the present invention.

Detailed ways

The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts 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. The indicated orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicated device or element must have a specific orientation or a specific orientation. construction and operation, and therefore should not be construed as limiting the invention. Furthermore, the terms "first", "second", and "third" are used for descriptive purposes only and should not be construed to indicate or imply relative importance.

In the description of the present invention, it should be noted that the terms "installed", "connected" and "connected" should be understood in a broad sense, unless otherwise expressly specified and limited, for example, it may be a fixed connection or a detachable connection connection, or integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal connection of two components, which can be a wireless connection or a wired connection connect. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood in specific situations.

In addition, the technical features involved in the different embodiments of the present invention described below can 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 that takes into account new energy consumption, as shown in FIG. 1 , including the following steps:

Step S1: Obtain the predicted value of the new energy power at the next moment and the predicted value of the new energy load at the next moment.

In a specific embodiment, 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 are obtained through the following steps:

Step S11: Obtain the measured values of the new energy power and load power at the current moment and the predicted values of the new energy power and the load power at the current moment at the previous moment.

Step S12 : correcting the error by the method of rolling optimization, and predicting the new energy power and load power at the next moment.

In the embodiment of the present invention, for the energy storage control process for a period of time, when the optimization period is sufficiently small, the new energy power and load power change curves can be assumed to be composed of multiple broken lines. Taking photovoltaic power prediction as an example, set At time t, the measured value of photovoltaic power is P _pv-t , and the predicted value of photovoltaic power at time t+1 is P _{pv-t+T-predict} , the measured value of photovoltaic power at time t+T is P _pv-T+1 , Then the prediction error ΔP _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 at time t+2T can be predicted Value is:

P _{pv-t+2T-predict} =P _pv-t+T +dP _pv +ΔP _pv .

The prediction principle of load power is the same as that of photovoltaic power prediction, and will not be repeated here.

Compared with the current energy storage control method, this embodiment increases the rolling prediction of new energy power and load power, makes full use of the latest measured data, and improves the accuracy of control. At the same time, the rolling optimization method is used to predict the new energy generation power and load power, which has low requirements on historical data and is suitable for the rapid deployment of new energy power plants.

Step S2: Based on 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, an energy storage optimization control model is constructed with the maximum new energy consumption within a preset time as the objective function.

In a specific embodiment, the description of the energy storage optimization control model is divided into three parts: objective function, constraints and optimization variables.

The objective function is described as follows: the objective function is solved according to the purpose of the present invention, and the maximum consumption of new energy in a period of time is the objective function, and its expression is:

Among them, P _pv-predict and P _load-predict are the photovoltaic power prediction value and load power prediction value, respectively, SOE _i and SOE _j are the energy storage battery energy states of energy storage battery i and energy storage battery j (State of Energy, (abbreviated as SOE), P _i and P _j are the average power of energy storage battery i and energy storage battery j in the optimization period, respectively, ω ₁ and ω ₂ are the reduction of new energy waste in the objective function and the SOE balance of energy storage battery. The influence weight coefficient occupied, n is the number of energy storage batteries, and T is the optimization period.

Further, the constraints are described as follows: according to the known conditions and physical constraints, the constraints for the optimization object are listed, including 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:

Among them, P _imin is the minimum value of the average power of the energy storage battery i in the optimization period, P _imax is the maximum value of the average power in the optimization period of the energy storage battery i, and SOE _imin is the minimum value of the energy state of the energy storage battery i of the energy storage battery , SOE _imax is the maximum value of the energy state of the energy storage battery i.

Further, the optimization variable is described as follows: the optimization variable in this embodiment is the output P _i of the energy storage battery.

Step S3: Based on the solution value of the energy storage optimization control model, a force command value is sent to the energy storage battery.

In a specific embodiment, when the optimization problem has a solution, the corresponding solution value can be sent to the energy storage battery as the output command value of the energy storage battery. When the optimization problem has no solution, that is, when the energy storage optimization control model has no solution value, the power shortage is evenly distributed according to the number of energy storage batteries, and issued as the output command value of the energy storage batteries. When there is no solution, the output calculation expression of the energy storage battery is:

P _i =(P _load-predict -P _pv-predict )/n.

In one embodiment, the energy storage control method considering new energy consumption further includes: after executing the step of issuing a force command value to the energy storage battery based on the solution value of the energy storage optimization control model, returning to obtain new energy Steps of power forecast value and new energy load power forecast value.

In a specific embodiment, after executing the step of sending the force command value to the energy storage battery based on the solution value of the energy storage optimization control model, it means that one optimization cycle ends. When one optimization period ends, enter the next optimization period, and go to step S1.

The invention provides an energy storage control method that takes into account new energy consumption, comprising: obtaining a new energy power prediction value at the next moment and a new energy load power prediction value at the next moment; Based on the predicted value of the new energy load power at the next moment, the energy storage optimization control model is constructed with the maximum new energy consumption in the preset time as the objective function; based on the solution value of the energy storage optimization control model, the force command value is sent to the energy storage battery. By predicting the short-term new energy power and load power, an energy storage optimization model is established with the minimum new energy disposal as the optimization goal, and the model is solved to achieve the optimal control effect and effectively reduce the new energy disposal rate. At the same time, the SOE of the energy storage batteries can be converged, the number of charging and discharging times of the energy storage batteries can be reduced, and the life of the energy storage batteries can be prolonged.

In one embodiment, the case model consists of 2 energy storage batteries and 1 photovoltaic power station.

This embodiment includes two main steps: power prediction and optimization solution.

Step 1: Power prediction. In this embodiment, the method of rolling optimization is used to predict the photovoltaic power and load. For a period of energy storage control process, when the optimization period is sufficiently small (2 seconds in the case of the present invention), the photovoltaic and load power change curves can be assumed to be It consists of multiple polylines, as shown in Figure 2.

At time t, the measured value of photovoltaic power is P _pv-t , the predicted value of photovoltaic power at time t+1 is P _{p-t+T-predict} , and the measured value of photovoltaic power at time t+T is P _pv-T+1 , then the prediction error ΔP _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 PV at time t+2T can be predicted The power value is:

P _{pv-t+2T-predict} =P _pv-t+T +dP _pv +ΔP _pv .

The prediction principle of load power is the same as that of photovoltaic power prediction, and will not be repeated here.

Step 2: Optimize the solution process:

Step 201: Describe the optimization model. The description of the optimization model is divided into three parts: objective function, constraints and optimization variables.

1) Describe the objective function. In this case, the predicted value of photovoltaic power generation is P _pv-predict , the predicted value of load power is P _load-predict , the output command of energy storage battery 1 is P ₁ , and the output command of 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 , and the optimization period is 2s.

Assuming that the SOE of the energy storage battery at time t is SOE _t , and the output of the energy storage battery in the time period from t to t+T is P, then the SOE of the energy storage battery at time t+T should be:

Taking the influence weights ω ₁ and ω ₂ of reducing new energy disposal and SOE balance of energy storage batteries as 0.8 and 0.2, respectively, and the optimization period T as 2s, the objective function can be expressed as:

2) Describe the constraints. The constraints in this case include the output constraint of the energy storage battery and the SOE constraint of the energy storage battery. The constraint expression in this example is as follows:

3) Describe the optimization variables. In this case, the optimized variables for the optimization solution are that the output command of the energy storage battery 1 is P ₁ , and the output command of the energy storage battery 2 is P ₂ .

Step 22: Issue the output command of the energy storage battery. According to whether the optimization problem has a solution, the command value is issued to the energy storage battery according to different strategies.

1) When the optimization problem has a solution, the corresponding solution value can be sent to the energy storage battery as the output command value of the energy storage battery.

2) When there is no solution to the optimization problem, the power shortage is evenly distributed according to the number of energy storage batteries, and issued as the output command value of the energy storage batteries. The calculation expression is:

P ₁ =P ₂ =(P _load-predict -P _pv-predict )/2

So far, one optimization cycle is over, and when the next optimization cycle starts, go to step 1 for power prediction.

An embodiment of the present invention also provides an energy storage control system that takes into account new energy consumption, as shown in FIG. 3 , including:

The rolling prediction module 1 is used to obtain the predicted value of new energy power and the predicted value of new energy load power. For details, refer to the relevant description of step S1 in the above embodiment, and details are not repeated here.

The model building module 2 is used for constructing an energy storage optimization control model based on the predicted value of the new energy power and the predicted value of the new energy load power, and taking the maximum new energy consumption within a preset time as the objective function. For details, refer to the relevant description of step S2 in the foregoing embodiment, which will not be repeated here.

The command issuing module 3 is used to issue a force command value to the energy storage battery based on the solution value of the energy storage optimization control model. For details, please refer to the relevant description of step S3 in the above embodiment, which will not be repeated here.

An embodiment of the present invention also 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 through a bus or in other ways. Take bus connection as an example.

The processor 61 may be a central processing unit (Central Processing Unit, CPU). The processor 61 may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) or Other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components and other chips, or a combination of the above types of chips.

As a non-transitory computer-readable storage medium, the memory 62 can be used to store non-transitory software programs, non-transitory computer-executable programs and modules, such as corresponding program instructions/modules in the embodiments of the present invention. The processor 61 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory 62, that is, to realize the storage taking into account the consumption of new energy in the above method embodiments. control method.

The memory 62 may include a storage program area and a storage data area, wherein the storage program area may store an operating system and an application program required by at least one function; the storage data area may store data created by the processor 61 and the like. Additionally, 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, memory 62 may optionally include memory located remotely from processor 61, which may be connected to processor 61 via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

One or more modules are stored in the memory 62, and when executed by the processor 61, execute the energy storage control method taking into account the consumption of new energy in the embodiment.

The specific details of the above computer equipment can be understood by referring to the corresponding related descriptions and effects in the embodiments, and details are not repeated here.

Those skilled in the art can understand that the realization of all or part of the processes in the methods of the above embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and the program can be executed when the program is executed. , may include the flow of the above-mentioned method embodiments. Wherein, the storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive) , abbreviation: HDD) or solid-state hard disk (Solid-State Drive, SSD), etc.; the storage medium may also include a combination of the above-mentioned types of memories.

Obviously, the above-mentioned embodiments are only examples for clear description, and are not intended to limit the implementation manner. For those of ordinary skill in the art, changes or modifications in other different forms can also be made on the basis of the above description. There is no need and cannot be exhaustive of all implementations here. And the obvious changes or changes derived from this are still within the protection scope of the present 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:

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:

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.

Images