A method, system, device and storage medium for controlling a cascade utilization energy storage system

Abstract

A kind of cascade utilization energy storage system control method, system, equipment and storage medium, according to new energy power station preset time prediction power error function and state of charge related objective function, obtain the current time new energy plan output cascade utilization energy storage system charge-discharge control coefficient;According to the current time new energy plan output cascade utilization energy storage system charge-discharge control coefficient, the current new energy power station generation actual power and predicted future preset time period power, determine the charge-discharge power of current time energy storage battery;According to the charge-discharge power of current time energy storage battery, control cascade utilization energy storage system.By determining the objective function for improving new energy power station tracking plan output ability, effectively utilize the difference between power station power day-ahead forecast data and real-time output data, real-time optimization control is carried out to cascade utilization power battery energy storage system charge-discharge control coefficient.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of wind power photovoltaic power generation, energy storage technology and new energy vehicles, and particularly relates to a cascade utilization energy storage system control method, system, device and storage medium. BACKGROUND

[0002] New energy power stations taking wind power, solar energy and other renewable energy as the power generation basis have realized the goal of low energy consumption, high cleanliness and environmental friendliness of traditional thermal power plants, and are one of the main sources of energy consumption in the future. The new energy power station can reduce the influence of the new energy power station on the power system, improve the power quality, and improve the stability and safety of the power grid by uniformly arranging the renewable energy power generation power.

[0003] The new energy power station can effectively convert unstable power generation mode into stable and reliable energy with the cooperation of the energy storage battery, help the power grid system dispatching department to uniformly arrange the coordination between new energy power stations to a certain extent, and develop a new energy power generation system dispatching plan, so that the new energy power generation system safely operates within the controllable range of the dispatching, and cooperates with the energy storage battery to generate joint power to arrange a suitable grid-connected operation mode. To a certain extent, the new energy power generation is stably operated according to the dispatching plan, the influence of the new energy power generation on the power system is reduced, the power quality is improved, and the stability and safety of the power grid are improved.

[0004] In the new energy power station based on the cascade utilization of power batteries, how to manage the energy of the multi-type energy storage system composed of different health states to achieve the goal of meeting the planned power output range and maximize the benefits of the new energy power station is a core problem. At present, there is a lack of new energy power station control technology for the cascade utilization of power batteries and joint energy storage system, and how to maximize the benefits of the new energy power station based on the cascade utilization of power batteries is a key problem.

[0005] At present, there is a deviation between the actual output and the planned output of the distributed power supply, which leads to a decrease in supply and demand reliability and other problems. SUMMARY

[0006] The purpose of the present application is to provide a cascade utilization energy storage system control method, system, device and storage medium, which can effectively utilize the retired energy storage battery as the energy storage system of the new energy power station and guarantee that the new energy meets the planned power output requirement to achieve maximum benefits, thereby realizing the purpose of cascade utilization energy storage system control based on the planned power output of the new energy.

[0007] In order to achieve the above-mentioned purpose of the application, the technical scheme adopted by the present application is as follows:

[0008] A cascade utilization energy storage system control method, comprising the following steps:

[0009] acquire the actual power of the new energy power station and the predicted power in a preset time period in the future;

[0010] obtain the charging and discharging control coefficient of the energy storage system for the current time according to the error function of the predicted power of the new energy power station in the preset time and the target function related to the state of charge;

[0011] determine the charging and discharging power of the energy storage battery at the current time according to the charging and discharging control coefficient of the energy storage system for the current time, the actual power of the new energy power station and the predicted power in a preset time period in the future;

[0012] control the energy storage system according to the charging and discharging power of the energy storage battery at the current time.

[0013] Further, the error function of the predicted power of the new energy power station in the preset time period is:

[0014]

[0015] In the formula, f1 is the tracking effect evaluation index value, P w (k) is the actual power of the new energy power station at k time, i is the prediction tracking number, m is the total number of control prediction tracking, P BESS (k) is the output value of the battery energy storage system after adaptive processing at k time, P f_mid (k) is the wind range control power at k time.

[0016] Further, the target function related to the state of charge is:

[0017]

[0018] In the formula, f2 is the SOC fluctuation evaluation index value of smooth battery output, SOC(k) is the state of charge of the energy storage battery at k time, soc max is the upper limit value of the state of charge of the energy storage battery, soc min is the lower limit value of the state of charge of the energy storage battery, and Δk is the time period difference of tracking planned output collection.

[0019] Further, the state of charge of the energy storage battery at the current time is:

[0020]

[0021] In the formula, SOC(k) is the state of charge value at k time, SOC(k-1) is the state of charge value at k-1 time, λ is the charging and discharging efficiency, P BESS is the output value of the energy storage battery after adaptive output at k time, and Δk is the time interval between two states. BESSThe capacity of the energy storage battery.

[0022] Further, the particle swarm optimization algorithm is adopted to obtain the step utilization energy storage system charge-discharge control coefficient of the new energy plan output at the current time according to the new energy power station short-term prediction power error function and the state of charge related objective function.

[0023] Further, the preset time period is 4 hours.

[0024] A step utilization energy storage system control system comprises:

[0025] A power acquisition module is configured to acquire the actual power of the current new energy power station and the predicted power of the future preset time period;

[0026] A charge-discharge control coefficient acquisition module is configured to obtain the step utilization energy storage system charge-discharge control coefficient of the new energy plan output at the current time according to the new energy power station preset time prediction power error function and the state of charge related objective function;

[0027] A charge-discharge power determination module is configured to determine the charge-discharge power of the energy storage battery at the current time according to the step utilization energy storage system charge-discharge control coefficient of the new energy plan output at the current time, the actual power of the current new energy power station and the predicted power of the future preset time period;

[0028] A control module is configured to control the step utilization energy storage system according to the charge-discharge power of the energy storage battery at the current time.

[0029] A computer device comprises a memory and a processor, and the memory stores a computer program capable of running on the processor.

[0030] A computer readable storage medium stores a computer program, and the computer program causes the processor to execute the step utilization energy storage system control method when executed by the processor.

[0031] Compared with the prior art, the beneficial effects of the present application are as follows:

[0032] This invention proposes an energy optimization control method for cascaded-utilization power battery energy storage systems to improve the planned output of new energy power plants. In the control of cascaded-utilization power battery energy storage systems, by determining an objective function to improve the planned output capacity of the new energy power plant, the difference between the day-ahead power forecast data and real-time output data is effectively utilized to optimize the charge and discharge control coefficients of the cascaded-utilization power battery energy storage system in real time. Compared with the general fixed-coefficient control approach, this invention enables the cascaded-utilization power battery energy storage system to better exert its flexibility, achieving the desired control effect, and improving economic efficiency in the practical application of new energy power plants equipped with cascaded-utilization power battery energy storage systems.

[0033] Furthermore, this invention is based on the particle swarm optimization algorithm, and uses the particle swarm algorithm to control and optimize the planned output of the new energy power plant and the cascade utilization power battery energy storage system in real time. It coordinates and optimizes the real-time charging and discharging of the cascade utilization power battery to meet the requirements of the tracking and processing plan. This achieves the goal of controlling the cascade utilization energy storage system based on the planned output of new energy. Attached Figure Description

[0034] Figure 1 This is a flowchart of a cascaded energy storage system control method according to the present invention.

[0035] Figure 2 This is a schematic diagram of the system of the present invention. Detailed Implementation

[0036] The invention will now be further described with reference to the accompanying drawings.

[0037] This invention proposes an energy management method for a new energy power station based on a cascaded utilization power battery combined energy storage system using a particle swarm optimization algorithm. It adds five control coefficients to reasonably control the cascaded utilization power battery combined energy storage system, and tracks the planned output of the new energy power station in real time to reduce the difference between the actual output and the planned output.

[0038] To address the shortcomings of the initial lifespan of retired power batteries used in tiered applications, resulting in relatively weak power supply reliability but low investment costs, this invention aims to maximize the revenue of new energy power plants. The objective function includes the revenue of new energy power plants, the cost of recycling tiered power batteries, and the penalty cost for power plant output errors. An adaptive power control strategy and weighting coefficients are applied, while also considering the SOC (supercapacitor state of charge) of the tiered power battery combined with energy storage system to keep it in a healthy state and extend its lifespan.

[0039] In this invention, the energy storage battery is a cascaded power battery.

[0040] A control method for a cascaded energy storage system based on renewable energy project output includes the following steps:

[0041] Obtain the actual power generation P of the new energy power station at the current time (time k). w (k) Predict the power output for the next preset time period (4 hours), the new energy power generation power output predicted in the previous day, and the state of charge of the energy storage battery at the current moment (initial moment).

[0042] Based on the actual power generation P of the new energy power station at time k. w (k) Predict the power in the future preset time period (4 hours), the new energy power generation power predicted in the previous day and the state of charge of the energy storage battery at the current moment (initial moment), and establish the short-term prediction power error function of the new energy power station and the objective function related to the state of charge;

[0043] The short-term predicted power error function for the new energy power plant is as follows:

[0044]

[0045] In the formula, f1 is the value of the tracking effect evaluation index, and P w (k) represents the actual power generation of the new energy power station at time k, i represents the number of prediction tracking attempts, m represents the total number of control prediction tracking attempts, and P BESS (k) represents the output value of the battery energy storage system after adaptive processing at time k, P f_mid (k) represents the wind range control power at time k.

[0046] The objective function related to the state of charge is as follows:

[0047]

[0048] In the formula, f2 is the SOC fluctuation evaluation index value for smoothing battery output, SOC(k) is the state of charge of the energy storage battery at time k, and soc max This refers to the upper limit of the state of charge (SOC) of the energy storage battery. min Δk represents the minimum charge level of the energy storage battery, and Δk represents the time difference for collecting data on the planned output.

[0049] The particle swarm optimization algorithm is used to solve the predicted power error function and the objective function related to the state of charge of the new energy power plant for a preset time period. The charging and discharging control coefficients of the cascaded energy storage system for the planned output of new energy at time k are obtained, namely the first charging and discharging control coefficient m, the second charging and discharging control coefficient n, the third charging and discharging control coefficient z, the fourth charging and discharging control coefficient s and the fifth charging and discharging control coefficient l.

[0050] Based on the charging and discharging control coefficient of the cascaded utilization energy storage system of the new energy planned output at time k, and the actual power generation P of the new energy power station at time k... w (k) and predict the power in the next 4 hours to determine the charging and discharging power of the energy storage battery at time k (i.e., the current time).

[0051] Example 1

[0052] See Figure 1 The specific steps of the control method for the tiered utilization of energy storage systems in the new energy plan are as follows:

[0053] Step 1: Initialize the iterative system time k i =1;

[0054] Step 2: Input the actual power generation P of the new energy power station at time k. w (k) Forecast power for the next 4 hours, forecast power of new energy generation for the day before, and state of charge of energy storage batteries at the current moment (initial moment);

[0055] The four power parameters involved in the recent forecast of renewable energy power generation are given by the following formula:

[0056] P limit =εC cap (1)

[0057] P f_max (k)=P f (k)+P limit (2)

[0058] P f_min (k)=P f (k)-P limit (3)

[0059] P f_mid (k)=P f_min (k)+s(P f_max -P f_min (4)

[0060] In the formula, P limit P represents the lower limit of the day-ahead forecast power generation of new energy power plants. f_max P represents the upper limit of the predicted power output before the power generation date. f_min P is the lower limit of the predicted power output before the power generation date. f (k) represents the predicted power output before the power generation date, P BESS (k) represents the day-ahead forecast power of new energy generation at time k, P f_mid C represents the adaptive power value, ε is the allowable percentage of the prediction error before the power generation date of the renewable energy power plant, and C is the adaptive power value. capdenoted as the installed capacity of the new energy power station, and s as the fourth charge-discharge control coefficient.

[0061] The current (initial) state of charge (SOC(k) of the energy storage battery is as follows:

[0062]

[0063] In the formula, SOC(k) is the state of charge value at time k, SOC(k-1) is the state of charge value at time k-1, λ is the charge / discharge efficiency, which is selected as 0.7 in this invention, P BESS Let C be the output value of the energy storage battery after adaptive output at time k, where Δk is the time interval between the two states. BESS This refers to the capacity of the energy storage battery.

[0064] Step 3: Establish the short-term predicted power error function and the state-of-charge related objective function of the new energy power plant. Use the particle swarm optimization algorithm to solve the short-term predicted power error function and the state-of-charge related objective function of the new energy power plant to obtain the system charge and discharge control coefficients at time k. The system charge and discharge control coefficients include the first charge and discharge control coefficient m, the second charge and discharge control coefficient n, the third charge and discharge control coefficient z, the fourth charge and discharge control coefficient s, and the fifth charge and discharge control coefficient l.

[0065] The specific process of solving the predicted power error function and the state-of-charge related objective function of the new energy power plant for a preset time period (4 hours) using the particle swarm optimization algorithm adopted in this invention is as follows:

[0066] Step 3-1: Read the predicted output data of short-term renewable energy power plants, formulate the upper and lower limits of the dispatch plan output, simulate the actual power generation output of renewable energy power plants on the day, and set the target value for tracking the power generation control of renewable energy power plants on the day;

[0067] Set the PSO control parameters: the total number of particles is M, the inertia constant is α, and the algorithm learning factors include the first algorithm learning factor β1 and the second algorithm learning factor β2.

[0068] Step 3-2: Initialize the position and velocity of each particle in the particle swarm. Set the iteration number a = 0; (particles include those corresponding to the first charge / discharge control coefficient m, the second charge / discharge control coefficient n, the third charge / discharge control coefficient z, the fourth charge / discharge control coefficient s, and the fifth charge / discharge control coefficient l, where the position is x) k The initial positions of the first charge / discharge control coefficient m, the second charge / discharge control coefficient n, the third charge / discharge control coefficient z, the fourth charge / discharge control coefficient s, and the fifth charge / discharge control coefficient l are set to 0.5; the particle velocity is v. k The initial velocities of the five particles corresponding to the respective charge / discharge coefficients are all randomly selected.

[0069] Step 3-3: Record and evaluate the fitness of each particle.

[0070] Steps 3-4: Record extreme values. Record the following extreme values: P BESTK J(P) represents the individual extreme value of the particle at the current iteration number; BESTK G is the objective function corresponding to this extreme value; BEST The global extreme value determined from the individual extreme values; J(G BEST ) represents the objective function value corresponding to the overall extremum.

[0071] if If it is established, then otherwise but Established, and P BESTK These represent the particle's position and individual extreme value at iteration number k.

[0072] Steps 3-5: Iterate k = k + 1, and update the velocity and position of each particle.

[0073]

[0074]

[0075] In the formula, —Particle velocity at iteration number k.

[0076] Steps 3-6: Calculate the target value for each particle at this moment again, and compare it with the target value of the previous iteration to determine whether it is necessary to update the individual extreme value or the overall extreme value.

[0077] Step 3-7: Determine if the objective function has converged. The iteration stops when the global best position remains unchanged for 100 consecutive iterations or the predefined maximum number of iterations is reached; otherwise, proceed to step 3-8.

[0078] Steps 3-8: Output the results.

[0079] The first charge / discharge control coefficient m and the second charge / discharge control coefficient n are used to control the active range of the battery energy storage system's SOC to optimize the reasonable tracking of the battery energy storage system's wind power output and better adapt to changes in the battery energy storage system's state of charge. The third charge / discharge control coefficient z is a coefficient that controls the upper and lower limits of the planned output. The fourth charge / discharge control coefficient s determines an adaptive power limit value between the lower and upper power limits at time t. Both the third charge / discharge control coefficient z and the fifth charge / discharge control coefficient l are random numbers between 0 and 1.

[0080] Step 4: Based on ki The moment-to-moment charge / discharge control coefficient of energy storage battery, the short-term predicted power of new energy (i.e., the predicted power of new energy during the preset time period) and k i Determine the actual power generation capacity of the new energy power station at any given time, and determine k. i The charging and discharging power of the energy storage battery at any given time is calculated by solving for k. i The short-term power prediction error function and the objective function related to the state of charge of new energy power plants at any given time;

[0081] Step 5: Iteration time k = k i +1;

[0082] Step 6: Read the actual power of the new energy source at time k;

[0083] Step 7: Based on the short-term predicted power of the renewable energy power plant at time k, the actual power generation of the renewable energy power plant at time k, and k... i Determine the charge and discharge control coefficients of the energy storage battery at time k, determine the charge and discharge power of the power battery at time k, and solve the short-term predicted power error function and the objective function related to the state of charge of the new energy power station at time k.

[0084] Step 8: Determine if the value k is greater than k i The sum of the particle swarm size M and the total number of particles M; if not, return to step 5; if yes, proceed to the next step.

[0085] Step 9: Assign system time k i =k+1;

[0086] Step 10: Determine k i Has the maximum been reached? If not, return to step 2; otherwise, end the process.

[0087] Example 2

[0088] See Figure 2 A control system for a cascaded energy storage system includes:

[0089] The power acquisition module is used to acquire the actual power generation of the current new energy power plant and predict the power generation in a future preset time period;

[0090] The charge and discharge control coefficient acquisition module is used to obtain the charge and discharge control coefficients of the cascaded energy storage system for the current time based on the power error function predicted by the preset time of the new energy power plant and the objective function related to the state of charge.

[0091] The charging and discharging power determination module is used to determine the charging and discharging power of the energy storage battery at the current moment based on the charging and discharging control coefficient of the cascade utilization energy storage system of the new energy planned output at the current moment, the actual power generation of the current new energy power station, and the predicted power for a preset time period in the future.

[0092] The control module is used to control the secondary energy storage system based on the current charging and discharging power of the energy storage battery.

[0093] Example 3

[0094] A computer device includes a memory and a processor, the memory storing a computer program that can run on the processor, the computer program being executed by the processor to implement the steps of the cascaded energy storage system control method as described above.

[0095] Example 4

[0096] A computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the cascaded energy storage system control method as described above.

[0097] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0099] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0100] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A control method for a cascaded energy storage system, characterized in that, Includes the following steps: Obtain the current actual power generation of the new energy power plant and predict the power generation over a preset time period; Based on the preset time prediction power error function of the new energy power plant and the objective function related to the state of charge, the charging and discharging control coefficient of the cascaded energy storage system for the planned output of new energy at the current moment is obtained. Based on the charging and discharging control coefficient of the energy storage system for the secondary use of new energy planned output at the current moment, the actual power generation of the current new energy power station, and the predicted power for a future preset time period, the charging and discharging power of the energy storage battery at the current moment is determined. The energy storage system for secondary use is controlled based on the current charging and discharging power of the energy storage battery. The particle swarm optimization algorithm is used to obtain the charging and discharging control coefficients of the cascaded energy storage system for the current planned output of new energy power plants based on the short-term predicted power error function of new energy power plants and the objective function related to the state of charge. The short-term predicted power error function for the new energy power plant is as follows: In the formula, To track and evaluate the performance indicators, Let be the actual power generation of the new energy power station at time k, i be the number of prediction tracking attempts, and m be the total number of control prediction tracking attempts. Let k be the output value of the battery energy storage system after adaptive processing. The wind speed range control power at time k; The objective function related to the state of charge is as follows: In the formula, To smooth battery output Volatility evaluation index value for k The state of charge of the energy storage battery is constantly monitored. This represents the upper limit of the state of charge of the energy storage battery. This is the minimum value for the state of charge of the energy storage battery. To track the time difference in data collection for the planned effort.

2. The control method for a cascaded energy storage system according to claim 1, characterized in that, The current state of charge of the energy storage battery is: In the formula, for k The state of charge value at time t. for k The state of charge value at time -1 For charging and discharging efficiency, for k The output value of the energy storage battery after adaptive output at any time. The time interval between the two states is... This refers to the capacity of the energy storage battery.

3. The control method for a cascaded energy storage system according to claim 1, characterized in that, The preset time period is 4 hours.

4. A control system for a cascaded energy storage system, characterized in that, include: The power acquisition module is used to acquire the actual power generation of the current new energy power plant and predict the power generation in a future preset time period; The charge and discharge control coefficient acquisition module is used to obtain the charge and discharge control coefficients of the cascaded energy storage system for the current time based on the power error function predicted by the preset time of the new energy power plant and the objective function related to the state of charge. The charging and discharging power determination module is used to determine the charging and discharging power of the energy storage battery at the current moment based on the charging and discharging control coefficient of the cascade utilization energy storage system of the new energy planned output at the current moment, the actual power generation of the current new energy power station, and the predicted power for a preset time period in the future. The control module is used to control the secondary energy storage system based on the charging and discharging power of the energy storage battery at the current moment. The particle swarm optimization algorithm is used to obtain the charging and discharging control coefficients of the cascaded energy storage system for the current planned output of new energy power plants based on the short-term predicted power error function of new energy power plants and the objective function related to the state of charge. The short-term predicted power error function for the new energy power plant is as follows: In the formula, To track and evaluate the performance indicators, Let be the actual power generation of the new energy power station at time k, i be the number of prediction tracking attempts, and m be the total number of control prediction tracking attempts. Let k be the output value of the battery energy storage system after adaptive processing. The wind speed range control power at time k; The objective function related to the state of charge is as follows: In the formula, To smooth battery output Volatility evaluation index value for k The state of charge of the energy storage battery is constantly monitored. This represents the upper limit of the state of charge of the energy storage battery. This is the minimum value for the state of charge of the energy storage battery. To track the time difference in data collection for the planned effort.

5. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program that can run on the processor. When the computer program is executed by the processor, it implements the cascaded energy storage system control method according to any one of claims 1-2.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform the cascaded energy storage system control method according to any one of claims 1-2.

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