Micro-grid energy storage operation regulation method and apparatus, device, storage medium, and product

Abstract

A micro-grid energy storage operation regulation method and apparatus, a device, a storage medium, and a product. The method comprises: acquiring target operation data of a micro-grid to be regulated, the micro-grid to be regulated comprising a generator set and an energy storage system, and the target operation data comprising generator set data, energy storage system data, and historical load data; using the target operation data as an input to a particle swarm algorithm, and using output power of the energy storage system as an output of the particle swarm algorithm, to obtain target output power of the energy storage system in combination with the particle swarm algorithm; and regulating the micro-grid to be regulated on the basis of the target output power of the energy storage system.

Description

Microgrid energy storage operation and control methods, devices, equipment, storage media and products

[0001] This application claims priority to Chinese Patent Application No. 202411206551.3, filed with the Chinese Patent Office on August 30, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of power grid technology, and for example to a microgrid energy storage operation control method, device, equipment, storage medium and product. Background Technology

[0003] Against the backdrop of energy transition and the construction of new power systems, the role of small hydropower is receiving increasing attention. The difficulty in regulating the flow of small hydropower clusters presents challenges for the coordinated operation of actual microgrids. In mountainous areas, many small hydropower stations are run-of-river distributed power sources, and their power generation capacity is significantly affected by the uncertainty of watershed flow. Energy storage is a crucial element of microgrid systems containing small hydropower, expanding the controllable operating space of the microgrid, smoothing the output and load of new energy sources, and reducing power system fluctuations caused by the grid connection of numerous distributed resources. The configuration of energy storage needs to specifically consider the matching capability between the actual power and energy of the microgrid; the size of the energy storage capacity will affect the reliability and economy of the microgrid system. For microgrid systems containing small hydropower, the energy storage system plays a supporting role in the distribution network, providing bidirectional power support and energy transfer.

[0004] Current microgrid operation optimization primarily considers economic and reliability indicators for single-objective or multi-objective optimization, focusing mainly on the economic benefits of energy storage, renewable energy absorption capacity, and the safe and stable operation of the microgrid. How to provide actual demand-side energy storage technology support and regulation for microgrid systems containing small hydropower remains a pressing issue that needs to be addressed. Summary of the Invention

[0005] This application provides a method, device, equipment, storage medium, and product for the operation and control of microgrid energy storage, so as to achieve optimized control of microgrids containing energy storage systems.

[0006] According to one aspect of this application, a method for regulating and controlling the operation of energy storage in a microgrid is provided, comprising: acquiring target operating data of a microgrid to be regulated; wherein the microgrid to be regulated includes generator sets and an energy storage system, and the target operating data includes generator set data, energy storage system data, and historical load data; using the target operating data as input to a particle swarm optimization (PSO) algorithm, using the output power of the energy storage system as the output of the PSO algorithm, and combining the PSO algorithm to obtain the target output power of the energy storage system; and regulating the microgrid to be regulated based on the target output power of the energy storage system.

[0007] In one possible implementation, the generator sets of the microgrid to be regulated include photovoltaic generator sets and small hydropower units, and the generator set data includes photovoltaic generator set data and small hydropower unit data.

[0008] In one possible implementation, the optimization objective of the particle swarm optimization algorithm is determined based on the net load power fluctuation rate of the microgrid to be regulated and the expected operating cost of the microgrid.

[0009] In one possible implementation, the net load power fluctuation rate of the microgrid to be regulated is the ratio of the standard deviation of the net load power to the average net load power, and the expected operating cost of the microgrid to be regulated includes the operating cost of the energy storage system and the tie-line power exchange cost.

[0010] In one possible implementation, the target output power of the energy storage system includes the output power sequence of the energy storage system within a set future time period. Regulating the microgrid to be regulated based on the target output power of the energy storage system includes: determining the charging and discharging state and power of the energy storage system within the set future time period based on the output power sequence of the energy storage system within the set future time period.

[0011] In one possible implementation, the method further includes: evaluating the control results of the microgrid to be controlled.

[0012] According to another aspect of this application, a microgrid energy storage operation and control device is provided, comprising: a target operation data acquisition module, configured to acquire target operation data of a microgrid to be controlled; wherein the microgrid to be controlled includes generator sets and an energy storage system, and the target operation data includes generator set data, energy storage system data, and historical load data; a target output power acquisition module, configured to use the target operation data as input to a particle swarm optimization algorithm, use the output power of the energy storage system as output of the particle swarm optimization algorithm, and combine the particle swarm optimization algorithm to obtain the target output power of the energy storage system; and a microgrid control module, configured to control the microgrid to be controlled according to the target output power of the energy storage system.

[0013] Optionally, the generator sets of the microgrid to be regulated include photovoltaic generator sets and small hydropower units, and the generator set data includes photovoltaic generator set data and small hydropower unit data.

[0014] Optionally, the optimization objective of the particle swarm optimization algorithm is determined based on the net load power fluctuation rate of the microgrid to be regulated and the expected operating cost of the microgrid.

[0015] Optionally, the net load power fluctuation rate of the microgrid to be regulated is the ratio of the standard deviation of the net load power of the microgrid to the average net load power, and the expected operating cost of the microgrid to be regulated includes the operating cost of the energy storage system and the tie-line power exchange cost of the microgrid to be regulated.

[0016] Optionally, the target output power of the energy storage system includes the output power sequence of the energy storage system within a set future time period. The microgrid control module is further configured to: determine the charging and discharging state and power of the energy storage system within the set future time period based on the output power sequence of the energy storage system within the set future time period.

[0017] Optionally, the device also includes a control result evaluation module, configured to evaluate the control result of the microgrid to be controlled.

[0018] According to another aspect of this application, an electronic device is provided, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the microgrid energy storage operation control method according to any embodiment of this application.

[0019] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the microgrid energy storage operation control method described in any embodiment of this application.

[0020] According to another aspect of this application, a computer program product is provided, the computer program product including a computer program or instructions, which, when executed by a processor, implement the microgrid energy storage operation and control method described in any embodiment of this application.

[0021] The microgrid energy storage operation and control method disclosed in this application first obtains the target operation data of the microgrid to be controlled. The microgrid to be controlled includes generator sets and an energy storage system, and the target operation data includes generator set data, energy storage system data, and historical load data. Then, the target operation data is used as input to a particle swarm optimization (PSO) algorithm, and the output power of the energy storage system is used as the output of the PSO algorithm. Combining the PSO algorithm, the target output power of the energy storage system is obtained. Finally, the microgrid to be controlled is regulated based on the target output power of the energy storage system. This microgrid energy storage operation and control method utilizes the PSO algorithm to output the target output power of the energy storage system. By adjusting the output power of the energy storage system, accurate and scientific regulation of the microgrid to be controlled can be achieved. Attached Figure Description

[0022] Figure 1 is a flowchart of a microgrid energy storage operation and control method according to an embodiment of this application;

[0023] Figure 2 is a flowchart of an operation control method for a microgrid to be controlled using a particle swarm optimization algorithm according to an embodiment of this application;

[0024] Figure 3 is a schematic diagram of a microgrid energy storage operation control device according to an embodiment of this application;

[0025] Figure 4 is a schematic diagram of the structure of the electronic device for implementing the microgrid energy storage operation control method of the present application embodiment. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly described below with reference to the accompanying drawings. The described embodiments may be some or all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Figure 1 is a flowchart of a microgrid energy storage operation and control method provided in an embodiment of this application. This embodiment is applicable to the control of microgrids containing energy storage systems. The method can be executed by a microgrid energy storage operation and control device, which can be implemented using either hardware or software and can be configured in an electronic device. As shown in Figure 1, the method includes:

[0029] S110. Obtain the target operation data of the microgrid to be regulated.

[0030] The microgrid to be regulated includes generator sets and energy storage systems, and the target operating data includes generator set data, energy storage system data, and historical load data.

[0031] In one possible implementation, the microgrid to be regulated is a small-scale power generation and distribution system currently being regulated.

[0032] Optionally, the generator sets of the microgrid to be regulated include photovoltaic generator sets and small hydropower units, and the generator set data includes photovoltaic generator set data and small hydropower unit data.

[0033] In one possible implementation, the microgrid to be regulated may include photovoltaic (PV) generators, small hydropower units, and energy storage systems. PV generator and small hydropower unit data includes, but is not limited to, their power generation characteristics. Energy storage system data includes, but is not limited to, the output power characteristics of the energy storage system within the microgrid to be regulated. Historical load data includes, but is not limited to, the historical load power characteristics of the microgrid to be regulated. When operating and regulating the microgrid to be regulated, the power energy characteristics of its source and load can first be constructed. In actual power generation systems, the power generation of small hydropower, PV power, and load power all exhibit uncertainties. For example, small hydropower generation is affected by water flow, which has a certain degree of randomness and uncertainty; PV power generation is affected by solar radiation, which has a certain degree of randomness and uncertainty; and load power is affected by residential electricity consumption behavior, which also has a certain degree of randomness and uncertainty. Therefore, to more accurately operate and regulate the microgrid to be regulated, it is necessary to analyze the power energy characteristics of the microgrid system containing small hydropower and to model the uncertainties of the source and load power energy characteristics.

[0034] (1) Uncertainty Model of Small Hydropower Generation

[0035] The power generation of small hydropower is mainly affected by water flow; therefore, the power generation of small hydropower can be calculated using the following model: P WA (t)=ξ WA (t)η WA

[0036] In the formula: P WA (t) represents the power generation of the small hydropower station at time t, ξ WA (t) represents the water flow rate at time t, and η WA This is the water-to-electricity conversion coefficient.

[0037] The uncertainty of water flow affects the power generation of small hydropower plants, and this uncertainty can be addressed by following a normal distribution f. WA (ξ WA (t) is used to characterize the mean μ of the normal distribution of water flow. WA and variance It can be obtained by fitting historical data.

[0038] (2) Uncertainty Model of Photovoltaic Power Generation

[0039] Photovoltaic power generation is mainly affected by solar radiation intensity and shows a positive correlation. Ignoring the effect of temperature, photovoltaic power generation can be calculated using the following model: P PV (t)=I PV (t)η PV A

[0040] In the formula: P PV (t) represents the photovoltaic power generation at time t; I PV (t) represents the intensity of solar radiation at time t; η PV denoted as the photo-to-electric conversion coefficient; A represents the total photovoltaic radiation area.

[0041] The uncertainty in solar radiation intensity affects photovoltaic power generation, and this uncertainty can be addressed through the beta distribution f. PV (I PV (t) is used to characterize the mean μ of the beta distribution of light radiation intensity. PV and variance It can be obtained by fitting historical data.

[0042] (3) Uncertainty model of load power consumption

[0043] Load power P L The uncertainty of (t) can be addressed by the normal distribution f L (P L (t) is used to characterize the load power consumption, which is a normal distribution with mean μ. L and variance It can be obtained by fitting historical data.

[0044] (4) Net load power model

[0045] In this application, the net load power of the microgrid to be regulated refers to the difference between the sum of the energy storage system and the power generation of the microgrid to be regulated and the power consumption of the load. It characterizes the overall source-load power fluctuation of the microgrid to be regulated at each moment of the day. The net load power model is as follows: ΔP(t)=P BS (t)+P PV (t)+P WA (t)-P L (t)

[0046] In the formula: ΔP(t) is the net load power at time t, P PV (t) represents the photovoltaic power generation at time t, P WA (t) represents the power generation of the small hydropower station at time t, P L(t) represents the load power consumption at time t, P BS (t) represents the output power of the energy storage system at time t, P BS,cha (t) and P BS,dis (t) represents the charging and discharging power of the energy storage system at time t.

[0047] S120. The target operating data is used as the input of the particle swarm algorithm, and the output power of the energy storage system is used as the output of the particle swarm algorithm. The target output power of the energy storage system is obtained by combining the particle swarm algorithm.

[0048] Among them, Particle Swarm Optimization (PSO) is an optimization algorithm that simulates the foraging behavior of bird flocks to find the optimal solution to a problem.

[0049] In one possible implementation, a particle swarm optimization (PSO) algorithm is applied to solve the regulation problem of the microgrid to be regulated. The target operating data of the microgrid is used as the algorithm's input variable, and the output power of the energy storage system is used as the algorithm's output variable. The actual target output power obtained by the algorithm is the future output power of the energy storage system predicted based on the target operating data. By adjusting the output power of the energy storage system, optimized regulation of the microgrid to be regulated is achieved.

[0050] The optimization objective of the particle swarm optimization algorithm is determined based on the net load power fluctuation rate of the microgrid to be regulated and the expected operating cost of the microgrid.

[0051] In one possible implementation, the microgrid to be regulated, when operating in grid-connected mode, is affected by uncertainties from photovoltaic and small hydropower. Given the relatively small overall power capacity of the microgrid, during grid-connected source-load power optimization, efforts should be made to maintain power balance within the microgrid, suppress power fluctuations during regulation, and simultaneously minimize operating costs. The power fluctuations and operating costs of the microgrid to be regulated are characterized by the net load power fluctuation rate and the expected operating cost of the microgrid, respectively; therefore, these two variables are used as the optimization objectives of the particle swarm optimization algorithm.

[0052] In one possible implementation, the net load power fluctuation rate of the microgrid to be regulated is the ratio of the standard deviation of the net load power to the average net load power. The expected operating cost of the microgrid to be regulated includes the operating cost of the energy storage system and the tie-line power exchange cost.

[0053] In one possible implementation, the daily power generation of the microgrid to be regulated can be selected for optimization. The day is divided into 24 time periods, each lasting 1 hour, as nodes for analysis. The optimization objectives are net load power fluctuation rate and the expected operating cost of the microgrid.

[0054] 1) Define net load power fluctuation rate:

[0055] In the formula, It is the net load power fluctuation rate, which represents the degree of power balance within the microgrid, P aL (t) represents the standard deviation of net load power. The net load power average value can be used to characterize the dispersion and concentration of power fluctuations, respectively. T is the statistical period. The smaller the net load power fluctuation rate, the better the balance.

[0056] 2) The expected operating cost of a microgrid mainly includes the operating cost of the energy storage system and the power exchange cost of the tie-line, which can be represented as follows: minF0=F ope +F line

[0057] In the formula, F0 represents the expected operating cost of the microgrid, F ope For the operating cost of energy storage systems, F line This refers to the power switching cost of the tie line.

[0058] In one possible implementation, considering the different dimensions of the two optimization objectives—net load power fluctuation rate and microgrid expected operating cost—they can be normalized, resulting in the following normalization function:

[0059] In the formula, and F g These are the normalized objective function values ​​for net load power fluctuation and expected operating cost of the microgrid, respectively. and These are the minimum and maximum values ​​of the net load power fluctuation rate, respectively, F. 0,min and F 0,max These represent the minimum and maximum expected operating costs of the microgrid, respectively. Based on a normalization method, the objective function is transformed into a new objective model:

[0060] In the formula, ω is the inertia weight vector, and ω∈[0,1].

[0061] By characterizing the aforementioned objective function, an optimized decision-making scheme that balances load fluctuation response and economic indicators can be obtained based on the energy storage capacity configuration of the microgrid to be regulated. Due to the uncertainty of the source and load of the microgrid to be regulated, the bidirectional power exchange between the microgrid and the main grid is affected by net power fluctuations, exhibiting opposite characteristics under power surplus and deficit operation scenarios. At the load level, its main power sources are photovoltaic, small hydropower, and energy storage systems. Deficit capacity is purchased from the main grid, while surplus capacity is absorbed by energy storage and the main grid. This application considers prioritizing the use of distributed photovoltaic and small hydropower output to meet load demand, with surplus or deficit portions recycled by the energy storage system, and considers grid-connected power purchase and sale to ensure the safe and stable operation of the microgrid.

[0062] In one possible implementation, when using the particle swarm optimization algorithm to regulate the microgrid to be regulated, it is also necessary to set constraints for the algorithm:

[0063] 1) The power balance constraint of source and load in the grid-connected operation of the microgrid to be regulated is ΔP(t)=P BS,dis (t)-P BS,cha (t)-P L (t)+P PV (t)+P WA (t)

[0064] 2) Constraints on the power output state of the microgrid to be regulated

[0065] Microgrids under control may face situations where the actual output of distributed power sources such as hydropower, solar power, and energy storage is relatively low (e.g., during the dry season for small hydropower plants), with actual output falling below expected output while actual load demand exceeds expected electricity demand. Therefore, there are constraints on reserve capacity.

[0066] In the formula, ΔP represents the standby capacity value for electrical load. max (t) represents the upper limit of net load power, which is subject to the following limitations: ΔP min (t)≤ΔP(t)≤ΔP max (t)

[0067] In the formula, ΔP min (t) represents the lower limit of net load power.

[0068] Furthermore, the output of photovoltaic power generators, small hydropower generators, and energy storage systems are subject to boundary constraints; their output cannot exceed their maximum output or be less than their minimum output.

[0069] In the formula, and These represent the upper and lower limits of photovoltaic power output, defined by the photovoltaic power generation system output power model. and These represent the lower and upper limits of small hydropower output, defined by the small hydropower generation runoff and output model. and These represent the lower and upper limits of the energy storage system's output value, which are also the charging and discharging power limits of the energy storage system per unit time.

[0070] 3) Energy storage capacity constraints

[0071] In the formula, S SOC (t), S SOC (t-1) represent the state of charge of the energy storage system at time t and time t-1, respectively. These are the upper and lower limits of the capacity under the state of charge, η. e η d These represent the charging efficiency and discharging efficiency of the energy storage system, respectively, and E0 is the rated capacity of the energy storage system.

[0072] 4) Energy storage charging and discharging constraints

[0073] In the formula, These are the upper limits of the charging and discharging power of the energy storage system.

[0074] In one possible implementation, a particle swarm optimization (PSO) algorithm is used for optimization. Figure 2 is a flowchart of a method for operating and controlling a microgrid using a PSO algorithm according to an embodiment of this application. As shown in the figure, firstly, the target operating data of the microgrid to be controlled, including photovoltaic generator data, small hydropower generator data, energy storage system data, and historical load data, are acquired as input to the algorithm. The PSO algorithm is initialized by randomly selecting the position and velocity of particles and determining parameters such as learning factor, inertia coefficient, population size, dimension, and number of iterations. Then, the net load power fluctuation rate and the expected operating cost of the microgrid to be controlled are selected as the optimization objectives of the PSO algorithm. The fitness value of each particle is calculated based on its position and the objective function. Then, the velocity, position, and inertia coefficient of the particles are updated, the particle fitness values ​​are recalculated, and the extreme solutions of individuals and the population are updated. By iteratively calculating the particle fitness values ​​and updating the optimal solutions of the particles, the maximum number of iterations is reached, and the optimal solution set of the optimization objective is obtained.

[0075] S130. Regulate the microgrid to be regulated according to the target output power of the energy storage system.

[0076] In one possible implementation, the output variable of the particle swarm optimization algorithm is the output power of the energy storage system. Through iterative calculations of the algorithm, the target output power of the energy storage system can be extracted from the final optimal solution set.

[0077] Optionally, the target output power of the energy storage system includes the output power sequence of the energy storage system within a set future time period. The method for regulating the microgrid to be regulated based on the target output power of the energy storage system can be: determining the charging and discharging state and power of the energy storage system within the set future time period based on the output power sequence of the energy storage system within the set future time period.

[0078] In one possible implementation, the target output power of the energy storage system can be a set output power sequence for a future period, such as a power sequence consisting of the output power of the energy storage system for each hour of a future day. Based on this output power sequence, the charging and discharging state and power of the energy storage system for each hour of a future day can be adjusted so that the output power of the energy storage system matches the target output power output by the algorithm, thereby minimizing the fluctuation of the net load power of the microgrid to be regulated and minimizing the expected operating cost of the microgrid.

[0079] In one possible implementation, after regulating the microgrid to be regulated according to the target output power of the energy storage system, the regulation result of the microgrid to be regulated can also be evaluated.

[0080] In one possible implementation, after regulating the microgrid to be regulated based on the target output power of the energy storage system, the net load power fluctuation rate and the expected operating cost of the microgrid after regulation can be calculated, and the regulation result is evaluated based on the calculation results. If the evaluation result shows that both the net load power fluctuation rate and the expected operating cost of the microgrid meet the operating requirements of the microgrid to be regulated, then the current regulation result continues to be executed; if the evaluation result shows that the net load power fluctuation rate and / or the expected operating cost of the microgrid do not meet the operating requirements of the microgrid to be regulated, then the particle swarm optimization algorithm is optimized by adjusting algorithm parameters, and the target output power of the energy storage system is recalculated using the optimized particle swarm optimization algorithm until the regulation result of the microgrid to be regulated meets the operating requirements of the microgrid to be regulated.

[0081] The microgrid energy storage operation and control method disclosed in this application first obtains the target operating data of the microgrid to be controlled. The microgrid to be controlled includes generator sets and an energy storage system, and the target operating data includes generator set data, energy storage system data, and historical load data. Then, the target operating data is used as input to a particle swarm optimization (PSO) algorithm, and the output power of the energy storage system is used as the output of the PSO algorithm. Combining the PSO algorithm, the target output power of the energy storage system is obtained. Finally, the microgrid to be controlled is regulated based on the target output power of the energy storage system. This microgrid energy storage operation and control method utilizes the PSO algorithm to output the target output power of the energy storage system. By adjusting the output power of the energy storage system, accurate and scientific regulation of the microgrid to be controlled can be achieved. Furthermore, by considering the net load power fluctuation and expected operating cost of the microgrid in the objective optimization function of the PSO algorithm, power fluctuations can be suppressed and the operating cost of the microgrid can be reduced during regulation.

[0082] Figure 3 is a schematic diagram of a microgrid energy storage operation and control device provided in an embodiment of this application. As shown in Figure 3, the device includes: a target operation data acquisition module 310, a target output power acquisition module 320, and a microgrid control module 330 to be controlled.

[0083] The target operation data acquisition module 310 is configured to acquire the target operation data of the microgrid to be regulated.

[0084] The microgrid to be regulated includes generator sets and energy storage systems, and the target operating data includes generator set data, energy storage system data, and historical load data.

[0085] The target output power acquisition module 320 is configured to take the target operating data as the input of the particle swarm algorithm, take the output power of the energy storage system as the output of the particle swarm algorithm, and combine the particle swarm algorithm to obtain the target output power of the energy storage system.

[0086] The microgrid control module 330 is configured to control the microgrid based on the target output power of the energy storage system.

[0087] Optionally, the generator sets of the microgrid to be regulated include photovoltaic generator sets and small hydropower units, and the generator set data includes photovoltaic generator set data and small hydropower unit data.

[0088] Optionally, the optimization objective of the particle swarm optimization algorithm is determined based on the net load power fluctuation rate of the microgrid to be regulated and the expected operating cost of the microgrid.

[0089] Optionally, the net load power fluctuation rate of the microgrid to be regulated is the ratio of the standard deviation of the net load power of the microgrid to the average net load power. The expected operating cost of the microgrid to be regulated includes the operating cost of the energy storage system and the power exchange cost of the tie line.

[0090] Optionally, the target output power of the energy storage system includes the output power sequence of the energy storage system within a set future time period. The microgrid control module 330 is further configured to: determine the charging and discharging state and power of the energy storage system within the set future time period based on the output power sequence of the energy storage system within the set future time period.

[0091] Optionally, the device also includes a control result evaluation module 340, configured to evaluate the control results of the microgrid to be controlled.

[0092] The microgrid energy storage operation and control device provided in this application embodiment can execute the microgrid energy storage operation and control method provided in any embodiment of this application, and has the corresponding functional modules for executing the method.

[0093] Figure 4 shows a schematic diagram of the structure of an electronic device 10 that can be used to implement embodiments of this application. The electronic device can be any form of digital computer, such as a laptop computer, desktop computer, workbench, personal digital assistant, server, blade server, mainframe computer, and other suitable computer. The electronic device can also be any form of mobile device, such as a personal digital processor, cellular phone, smartphone, wearable device (e.g., helmet, glasses, watch, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0094] As shown in Figure 4, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0095] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0096] Processor 11 can be any general-purpose and / or special-purpose processing component with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), any special-purpose artificial intelligence (AI) computing chip, any processor running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs several of the methods and processes described above, such as microgrid energy storage operation and control methods.

[0097] In some embodiments, the microgrid energy storage operation control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded into and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the microgrid energy storage operation control described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the microgrid energy storage operation control method by any other suitable means (e.g., by means of firmware).

[0098] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0099] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0100] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the above. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A machine-readable storage medium may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fiber, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0101] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor) configured to display information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be configured to provide interaction with a user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0102] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0103] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is established by running computer programs on the respective computers that establish this relationship. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service ecosystem. It addresses the shortcomings of traditional physical hosts and Virtual Private Server (VPS) services, such as high management difficulty and weak business scalability.

Claims

1. A method for regulating and controlling the operation of energy storage in a microgrid, comprising: Acquire target operating data of the microgrid to be regulated; wherein the microgrid to be regulated includes generator sets and energy storage systems, and the target operating data includes generator set data, energy storage system data, and historical load data; The target operating data is used as the input of the particle swarm algorithm, and the output power of the energy storage system is used as the output of the particle swarm algorithm. By combining the particle swarm algorithm, the target output power of the energy storage system is obtained. The microgrid to be regulated is regulated according to the target output power of the energy storage system.

2. The method according to claim 1, wherein, The generator sets of the microgrid to be regulated include photovoltaic generator sets and small hydropower units, and the generator set data includes photovoltaic generator set data and small hydropower unit data.

3. The method according to claim 1, wherein, The optimization objective of the particle swarm optimization algorithm is determined based on the net load power fluctuation rate of the microgrid to be regulated and the expected operating cost of the microgrid.

4. The method according to claim 3, wherein, The net load power fluctuation rate of the microgrid to be regulated is the ratio of the standard deviation of the net load power to the average net load power. The expected operating cost of the microgrid to be regulated includes the operating cost of the energy storage system and the tie-line power exchange cost.

5. The method according to claim 1, wherein, The target output power of the energy storage system includes the output power sequence of the energy storage system within a set future time period. The regulation of the microgrid to be regulated based on the target output power of the energy storage system includes: Based on the output power sequence of the energy storage system within a set future time period, determine the charging and discharging state and power of the energy storage system within the set future time period.

6. The method according to claim 5, further comprising: The control results of the microgrid to be controlled are evaluated.

7. A microgrid energy storage operation control device, comprising: The target operation data acquisition module is configured to acquire the target operation data of the microgrid to be regulated; wherein, the microgrid to be regulated includes generator sets and energy storage systems, and the target operation data includes generator set data, energy storage system data, and historical load data; The target output power acquisition module is configured to take the target operating data as the input of the particle swarm algorithm, take the output power of the energy storage system as the output of the particle swarm algorithm, and combine the particle swarm algorithm to obtain the target output power of the energy storage system. The microgrid control module is configured to control the microgrid based on the target output power of the energy storage system.

8. An electronic device, the electronic device comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the microgrid energy storage operation and control method according to any one of claims 1-6.

9. A computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute the microgrid energy storage operation control method according to any one of claims 1-6.

10. A computer program product, comprising a computer program or instructions, wherein the computer program or instructions, when executed by a processor, implement the microgrid energy storage operation control method as described in any one of claims 1-6.

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