A method and system for energy storage control considering new energy consumption

Abstract

The application provides a kind of energy storage control method and system considering new energy accommodation, the method comprises: obtaining next time new energy power prediction value and next time new energy load power prediction value;Based on next time new energy power prediction value and next time new energy load power prediction value, with the maximum new energy accommodation in preset time as objective function, an energy storage optimization control model is constructed;Based on the solution value of the energy storage optimization control model, the power instruction value is issued to the energy storage battery.Through the prediction of short-term new energy power and load power, an energy storage optimization model is established with the minimum new energy abandonment as the optimization target, the model is solved, and the optimal control effect is achieved, which effectively reduces the new energy abandonment rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage control, and particularly relates to an energy storage control method and system considering new energy consumption. BACKGROUND

[0002] The load of a power grid presents the characteristics of double peaks in the daytime and a valley at night due to human activity habits. During the load valley period, too much wind power and photovoltaic power connected will cause power imbalance of the power system or long-time low-output operation and frequent start-stop of thermal power units, and thus the operation economy is poor, and therefore a large amount of wind power and photovoltaic power is abandoned. The existing energy storage control technology mostly optimizes the economy of the overall system of regional energy storage-power generation as the implementation target, and the improvement of the new energy abandonment phenomenon is limited. SUMMARY

[0003] Therefore, the technical problem to be solved by the present application is to overcome the defect that the existing energy storage control technology in the prior art has limited improvement on the new energy abandonment phenomenon, and thus provide an energy storage control method and system considering new energy consumption.

[0004] The technical scheme provided by the present application is as follows:

[0005] In a first aspect, an energy storage control method considering new energy consumption is provided, comprising:

[0006] obtaining a new energy power prediction value at a next time and a new energy load power prediction value at the next time;

[0007] based on the new energy power prediction value at the next time and the new energy load power prediction value at the next time, constructing an energy storage optimization control model with the maximum new energy consumption in a preset time as an objective function;

[0008] based on a solution value of the energy storage optimization control model, issuing a power instruction value to an energy storage battery.

[0009] Optionally, the energy storage control method considering new energy consumption further comprises:

[0010] 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 is issued as an energy storage battery power instruction value.

[0011] Optionally, the objective function is expressed as follows:

[0012]

[0013] wherein, P pv-predict and P load-predict are photovoltaic power prediction values and load power prediction values, SOE i and SOE jrespectively are the state of energy of the energy storage battery i and the energy storage battery j, P i and P j respectively are the average power of the energy storage battery i and the energy storage battery j in the optimization period, ω1 and ω2 are respectively the influence weight coefficients of the target function for reducing new energy abandonment and balancing the state of energy of the energy storage battery, n is the number of the energy storage batteries, and T is the optimization period.

[0014] Optionally, the constraint condition of the energy storage optimization control model is as follows:

[0015]

[0016] respectively are the state of energy of the energy storage battery i and the energy storage battery j, 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 of the energy storage battery i in the optimization period, SOE imin is the minimum value of the state of energy of the energy storage battery i, and SOE imax is the maximum value of the state of energy of the energy storage battery i.

[0017] Optionally, the step of obtaining the new energy power prediction value and the new energy load power prediction value at the next moment comprises the following steps.

[0018] obtaining the measurement values of the new energy power and the load power at the current moment and the prediction values of the new energy power and the load power at the current moment at the previous moment;

[0019] predicting the new energy power and the load power at the next moment by correcting errors through a rolling optimization method.

[0020] Optionally, the energy storage control method considering new energy consumption further comprises the following step: returning to the step of obtaining the new energy power prediction value and the new energy load power prediction value after the step of executing the solution value of the energy storage optimization control model to output the power instruction value of the energy storage battery.

[0021] Optionally, when the energy storage optimization control model has no solution value, the expression of the power instruction value of the energy storage battery is as follows:

[0022] P i = (P load-predict -P pv-predict ) / n.

[0023] In a second aspect, an embodiment of the present application provides an energy storage control system considering new energy consumption, comprising:

[0024] a rolling prediction module, configured to obtain a new energy power prediction value and a new energy load power prediction value;

[0025] A model construction module is configured to construct an energy storage optimization control model with a maximum new energy consumption in a preset time as an objective function based on the new energy power prediction value and the new energy load power prediction value.

[0026] An instruction issuing module is configured to issue a power instruction value to the energy storage battery based on a solution value of the energy storage optimization control model.

[0027] In a third aspect, a computer readable storage medium is provided, which stores computer instructions. The computer instructions are used to make a computer execute the energy storage control method considering new energy consumption according to the first aspect.

[0028] In a fourth aspect, a computer device is provided, which includes a memory and a processor. The memory and the processor are communicatively connected with each other. The memory stores computer instructions. The processor executes the computer instructions to execute the energy storage control method considering new energy consumption according to the first aspect.

[0029] The technical scheme of the present application has the following advantages:

[0030] The energy storage control method considering new energy consumption provided by the present application includes: obtaining a new energy power prediction value at a next time and a new energy load power prediction value at the next time; constructing an energy storage optimization control model with a maximum new energy consumption in a preset time as an objective function based on the new energy power prediction value at the next time and the new energy load power prediction value at the next time; and issuing a power instruction value to an energy storage battery based on a solution value of the energy storage optimization control model. The short-term new energy power and load power are predicted, an energy storage optimization model is established with a minimum new energy abandonment as an optimization objective, the model is solved, and the optimal control effect is achieved, so that the new energy abandonment rate is effectively reduced. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the specific embodiments of the present application or the technical scheme in the prior art, the drawings needed in the specific embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0032] Figure 1 A flow chart of a specific example of the energy storage control method considering new energy consumption in the embodiments of the present application;

[0033] Figure 2 A photovoltaic and load power change curve in the embodiments of the present application;

[0034] Figure 3 A principle block diagram of a specific example of the energy storage control system considering new energy consumption in the embodiment of the present application is shown in the figure;

[0035] Figure 4 A composition diagram of a specific example of the computer device provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0036] The technical solutions of the present application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of the present application.

[0037] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application. In addition, the terms "first", "second", "third" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0038] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the communication between two elements, or it can be wireless connection, or it can be wired connection. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0039] In addition, the technical features involved in different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0040] The embodiment of the present application provides a kind of energy storage control method considering new energy consumption, as shown in Figure 1 The method comprises the following steps:

[0041] Step S1: obtain the next time new energy power prediction value and the next time new energy load power prediction value.

[0042] In a specific embodiment, the next time new energy power prediction value and the next time new energy load power prediction value are obtained by the following steps:

[0043] Step S11: obtaining 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 at the previous moment.

[0044] Step S12: correcting the errors by the method of rolling optimization to predict the new energy power and the load power at the next moment.

[0045] In the embodiment of the present application, for the energy storage control process in a period of time, when the optimization period is small enough, the change curve of the new energy power and the load power can be assumed to be composed of multiple broken lines. Taking the photovoltaic power prediction as an example, it is assumed that at time t, the measured value of the photovoltaic power is P pv-t The predicted value of the photovoltaic power at time t+1 is P pv-t+T-predict The measured value of the photovoltaic power at time t+T is P pv-T+1 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 Therefore, the predicted value of the photovoltaic power at time t+2T is:

[0046] P pv-t+2T-predict = P pv-t+T +dP pv +ΔP pv .

[0047] The prediction principle of the load power is the same as that of the photovoltaic power prediction, which will not be described here.

[0048] Compared with the current energy storage control method, the embodiment increases the rolling prediction of the new energy power and the load power, fully utilizes the latest measured data, and improves the accuracy of the control. At the same time, the rolling optimization method is used to predict the new energy power and the load power, which has low requirements for historical data and is suitable for the rapid deployment scene of new energy power stations.

[0049] 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 in a preset time as an objective function.

[0050] In a specific embodiment, the description of the energy storage optimization control model is divided into three parts: objective function, constraint condition and optimization variable.

[0051] Among them, the objective function is described as follows: according to the solving purpose of the present application, the maximum new energy consumption in a period of time is taken as the objective function, and its expression is:

[0052]

[0053] wherein P pv-predict and P load-predict are the photovoltaic power prediction value and the load power prediction value, respectively, SOE i and SOE j are the state of energy (SOE) of the energy storage battery i and the energy storage battery j, respectively, P i and P j are the average power of the energy storage battery i and the energy storage battery j in the optimization period, ω1 and ω2 are the influence weight coefficients of the objective function for reducing new energy curtailment and balancing the SOE of the energy storage battery, respectively, n is the number of energy storage batteries, and T is the optimization period.

[0054] Further, the constraint conditions are described as follows: the constraint conditions for the optimization object are listed according to known conditions and physical constraints, 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:

[0055]

[0056] wherein 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 of the energy storage battery i in the optimization period, SOE imin is the minimum value of the SOE of the energy storage battery i, SOE imax is the maximum value of the SOE of the energy storage battery i.

[0057] Further, the optimization variables are described as follows: the optimization variables of the embodiment are the output P i of the energy storage battery.

[0058] Step S3: issuing the output instruction value of the energy storage battery based on the solution value of the energy storage optimization control model.

[0059] In a specific embodiment, when the optimization problem has a solution, the corresponding solution value can be issued to the energy storage battery as the output instruction value of the energy storage battery. When the optimization problem has no solution, i.e., 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 is issued as the output instruction value of the energy storage battery. The calculation expression of the output of the energy storage battery when there is no solution is as follows:

[0060] P i = (P load-predict -P pv-predict ) / n.

[0061] In an embodiment, the energy storage control method considering new energy consumption further comprises: after the step of executing the solution value based on the energy storage optimization control model to issue the power instruction value to the energy storage battery, returning to the step of obtaining the new energy power prediction value and the new energy load power prediction value.

[0062] In a specific embodiment, after the step of executing the solution value based on the energy storage optimization control model to issue the power instruction value to the energy storage battery, one optimization cycle ends. After one optimization cycle ends, the next optimization cycle is entered, and the step S1 is turned to.

[0063] The energy storage control method considering new energy consumption provided by the application comprises: obtaining a new energy power prediction value at the next moment and a new energy load power prediction value at the next moment; constructing an energy storage optimization control model with the maximum new energy consumption in a preset time as an objective function based on the new energy power prediction value at the next moment and the new energy load power prediction value at the next moment; and issuing a power instruction value to an energy storage battery based on the solution value of the energy storage optimization control model. The short-term new energy power and load power are predicted, the energy storage optimization model is established with the minimum new energy abandonment as an optimization objective, the model is solved, the optimal control effect is achieved, and the new energy abandonment rate is effectively reduced. At the same time, the SOE of 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.

[0064] In an embodiment, the case model is composed of two energy storage batteries and one photovoltaic power station.

[0065] The embodiment comprises two main steps: power prediction and optimization solving.

[0066] Step 1: power prediction. In the embodiment, the rolling optimization mode is used for photovoltaic power and load prediction. For a certain period of energy storage control process, when the optimization cycle is small enough (2 seconds in the case of the application), the photovoltaic and load power change curves can be assumed to be composed of multiple broken lines, as shown in FIG. 1. Figure 2

[0067] Suppose that at time t, the photovoltaic power measurement value is P pv-t , the photovoltaic power prediction value at time t+1 is P p-t+T-predict , and the photovoltaic power measurement value 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 , and the photovoltaic power value at time t+2T can be predicted as:

[0068] P pv-t+2T-predict =P​pv-t+T +dP pv +ΔP pv .

[0069] The prediction principle of load power is the same as that of photovoltaic power prediction, which will not be described here.

[0070] Step two: optimization solving process:

[0071] Step two zero one: describe the optimization model, which is divided into three parts: objective function, constraint condition and optimization variable.

[0072] 1) Describe the objective function, in this case, set the photovoltaic power prediction value as P pv-predict , the load power prediction value as P load-predict , the output instruction of energy storage battery 1 as P1, the output instruction of energy storage battery 2 as P2, the SOE of energy storage battery 1 as SOE1, the SOE of energy storage battery 2 as SOE2, and the optimization period as 2s.

[0073] Set the SOE of energy storage battery at time t as SOE t , the output of energy storage battery in the period from t to t+T as P, then the SOE of energy storage battery at time t+T should be:

[0074]

[0075] Take the influence weights ω1 and ω2 of reducing new energy abandonment and energy storage battery SOE balance as 0.8 and 0.2 respectively, and the optimization period T as 2s, then the objective function can be expressed as:

[0076]

[0077] 2) Describe the constraint condition, the constraint condition of this case includes energy storage battery output constraint and energy storage battery SOE constraint. In this example, the constraint condition expression is as follows:

[0078]

[0079] 3) Describe the optimization variable, in this case, the optimization variable of optimization solving is the output instruction of energy storage battery 1 as P1 and the output instruction of energy storage battery 2 as P2.

[0080] Step two zero two: issue the energy storage battery output instruction. According to whether the optimization problem has a solution, issue the instruction value to the energy storage battery according to different strategies.

[0081] 1) When the optimization problem has a solution, issue the corresponding solution value as the energy storage battery output instruction value.

[0082] 2) When the optimization problem has no solution, the power deficit is evenly distributed according to the number of energy storage batteries and issued as the output command value for the energy storage batteries. The calculation expression is:

[0083] P1=P2=(P load-predict -P pv-predict ) / 2

[0084] At this point, one optimization cycle has ended. When the next optimization cycle begins, we will proceed to step one for power prediction.

[0085] This invention also provides an energy storage control system that takes into account the consumption of new energy sources, such as... Figure 3 As shown, it includes:

[0086] Rolling forecast module 1 is used to obtain the predicted power output and load power output of new energy sources. For details, please refer to the relevant description of step S1 in the above embodiments, which will not be repeated here.

[0087] Model building module 2 is used to construct an energy storage optimization control model based on the predicted power and load power of new energy sources, with the objective function of maximizing the absorption of new energy sources within a preset time period. For details, please refer to the relevant description of step S2 in the above embodiments, which will not be repeated here.

[0088] Command issuing module 3 is used to issue force command values ​​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 embodiments, which will not be repeated here.

[0089] This invention also provides a computer device, such as... Figure 4 As shown, the device terminal may include a processor 61 and a memory 62, wherein the processor 61 and the memory 62 can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0090] Processor 61 can be a central processing unit (CPU). Processor 61 can 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 of the above types of chips.

[0091] The memory 62, as a non-transitory computer readable storage medium, 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 application. The processor 61 performs 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, implements the energy storage control method considering new energy consumption in the above-mentioned method embodiments.

[0092] The memory 62 can include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required by a function; and the data storage area can store data created by the processor 61 and the like. In addition, the memory 62 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory 62 can optionally include a memory disposed remotely with respect to the processor 61, and these remote memories can be connected to the processor 61 through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0093] One or more modules are stored in the memory 62, and when executed by the processor 61, the energy storage control method considering new energy consumption in the embodiments is performed.

[0094] The above-mentioned computer device specific details can be understood with reference to the corresponding related descriptions and effects in the embodiments, which will not be described here.

[0095] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The program can be stored in a computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid state drive (SSD), etc. The storage medium can also include a combination of the above-mentioned types of memories.

[0096] Obviously, the above embodiments are merely example for clearly illustrating but not limitation to the embodiments. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, all the embodiments need not and can not be enumerated. The obvious changes or variations derived from the above description are still within the protection scope of the present application.

Claims

1. An energy storage control method considering the consumption of new energy sources, characterized in that, include: Obtain the predicted power output of renewable energy sources and the predicted load power of renewable energy sources at the next time step; Based on the predicted power of new energy sources and the predicted load power of new energy sources at the next time step, an energy storage optimization control model is constructed with the objective function of maximizing the absorption of new energy sources within a preset time. Based on the solution value of the energy storage optimization control model, a force command value is issued to the energy storage battery; The objective function is expressed as follows: in, and These are the photovoltaic power forecast and the load power forecast, respectively. and Energy storage batteries i and energy storage batteries j The energy state of the energy storage battery, and Energy storage batteries i and energy storage batteries j Average power during the optimization period, and These are the respective weighting coefficients for reducing renewable energy abandonment and achieving SOE (Solar Energy Equilibrium) balance in the objective function. n For the number of energy storage batteries, To optimize the cycle.

2. The energy storage control method considering new energy consumption according to claim 1, characterized in that, Also includes: When the energy storage optimization control model has no solution, the power deficit is evenly distributed according to the number of energy storage batteries and issued as the output command value of the energy storage batteries.

3. The energy storage control method considering new energy consumption according to claim 1, characterized in that, The constraints of the energy storage optimization control model are as follows: in, For energy storage batteries i Minimum average power within the optimization period. For energy storage batteries i The average maximum power within the optimization period For energy storage batteries i The minimum value of the energy state of the energy storage battery. For energy storage batteries i The maximum value of the energy state of the energy storage battery.

4. The energy storage control method considering new energy consumption according to claim 1, characterized in that, The process of obtaining the predicted power output of renewable energy sources and the predicted load power of renewable energy sources at the next time step includes: Obtain the measured values ​​of renewable energy power and load power at the current moment, as well as the predicted values ​​of renewable energy power and load power at the previous moment for the current moment; Errors are corrected by rolling optimization to predict the power of new energy sources and the power of load at the next moment.

5. The energy storage control method considering new energy consumption according to claim 1, characterized in that, Also 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, the process returns to the step of obtaining the predicted value of new energy power and the predicted value of new energy load power.

6. The energy storage control method considering new energy consumption according to claim 2, characterized in that, When the energy storage optimization control model has no solution, the expression for calculating the energy storage battery output command value is as follows: 。 7. An energy storage control system that takes into account the consumption of new energy sources, characterized in that, include: The rolling forecast module is used to obtain the predicted values ​​of renewable energy power and renewable energy load power. The model building module is used to construct an energy storage optimization control model based on the predicted new energy power and the predicted new energy load power, with the objective function being the maximum absorption of new energy within a preset time. The command issuing module is used to issue force command values ​​to the energy storage battery based on the solution value of the energy storage optimization control model; The objective function is expressed as follows: in, and These are the photovoltaic power forecast and the load power forecast, respectively. and Energy storage batteries i and energy storage batteries j The energy state of the energy storage battery, and Energy storage batteries i and energy storage batteries j Average power during the optimization period, and These are the respective weighting coefficients for reducing renewable energy abandonment and achieving SOE (Solar Energy Equilibrium) balance in the objective function. n For the number of energy storage batteries, To optimize the cycle.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the energy storage control method for taking into account the consumption of new energy sources as described in any one of claims 1-6.

9. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the energy storage control method for considering renewable energy consumption as described in any one of claims 1-6.

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