Multi-energy complementary coordination control system and method based on multi-element energy storage

Abstract

The embodiment of the invention relates to a multi-energy complementary coordinated control system and method based on multi-element energy storage, electronic equipment and a storage medium. And various data support optimization decisions of multiple stations are obtained through data fusion. And a decision generation scheme is optimized, the operation state and benefit analysis are displayed, and precise regulation and control of the station are realized. And configuring a management algorithm for the model, and setting boundary conditions and objective functions. The internal coordination supplementary logic plays a role in the aspects of active power adjustment, power grid frequency fluctuation and synchronous phase modulation. Through the means, the acceptance degree of the power grid to renewable energy sources is improved, so that the multi-energy complementary system can better respond to a power grid dispatching instruction, and energy resource optimization management is realized. And meanwhile, the stability and reliability of the power grid are enhanced, the economic benefit of the system is improved, and a powerful guarantee is provided for efficient operation of the multi-energy complementary coordination control system.

Description

Technical Field

[0001] The present application relates to the field of coordinated control of power systems, and in particular to a multi-energy complementary coordinated control system, method, device, system, computer equipment and computer-readable storage medium based on multi-element energy storage. Background Art

[0002] Wind energy and solar energy are not only complementary in time, but also in space. Compared with wind power or photovoltaic power generation alone, the wind-solar complementary power generation system can make the power output more stable and improve the grid's absorption and acceptance of renewable energy.

[0003] Renewable energy generation depends on natural conditions, such as wind instability and changes in sunlight, which makes its power output unstable. This unstable power output may cause instability in grid voltage, frequency and other parameters, increase the difficulty of grid operation and management, and reduce the safety and reliability of grid operation. In addition, when the grid accepts renewable energy generation, it is difficult to effectively dispatch and control it due to its large power fluctuations, which limits the large-scale application and efficient use of renewable energy in the grid. Summary of the invention

[0004] The embodiments of the present application provide a multi-energy complementary coordinated control system, method, computer device and computer-readable storage medium based on multi-element energy storage, so as to at least solve the problem of unstable power output of new energy equipment in related technologies and reduce the safety and reliability of power grid operation.

[0005] In a first aspect, an embodiment of the present application provides a multi-energy complementary coordinated control system based on multi-element energy storage, the system comprising:

[0006] Data processing module, optimization decision module and model configuration module, among which:

[0007] The data processing module is used to obtain the operating data of each power station in the multi-energy complementary area, wherein the operating data includes wind power data, photovoltaic data, independent energy storage data and new energy + energy storage combined data;

[0008] The optimization decision module is used to obtain the optimization target, and based on the optimization target, the operating data and the time scale information, call the preset model through the model configuration module to generate an optimization plan, and regulate each power supply station within the multi-energy complementary area through the optimization plan.

[0009] In some embodiments, the model configuration module includes a station management module and an operation management module, wherein:

[0010] The station management module is used to manage the basic information of each station within the multi-energy complementary area, wherein the basic information includes: project number, power station name, affiliated unit, power station type, grid connection time, access time and area;

[0011] The operation management module is used to manage the control objects and index characteristics in each station within the multi-energy complementary area, wherein the control objects include photovoltaic object models, wind power object models and energy storage models, and the index characteristics include: boundary conditions, index characteristics, objective functions and optimization algorithms.

[0012] In some embodiments, the boundary conditions include: external environmental influencing factors, internal coordination characteristic influencing factors, wherein the external environmental influencing factors include: electricity receiving price, electricity purchasing price and auxiliary market transaction principle;

[0013] The internal coordination characteristic influencing factors are expressed by the following formula:

[0014]

[0015] Among them: Pload,t represents the output demand at time t, Pi,t represents the output power of the i-th renewable energy and renewable energy + energy storage combined station at time t, Pj,t represents the interactive power of the j-th energy storage station at time t, output is positive and input is negative;

[0016]

[0017] Among them, Pimax represents the predicted maximum output plan of the i-th new energy station;

[0018]

[0019] Among them, P1j,max represents the maximum value of the energy storage charging power in the tth period, and P2j,max represents the maximum value of the energy storage charging power in the tth period.

[0020] In some embodiments, the objective function is determined based on an economic objective and / or a volatility objective, wherein:

[0021] The function corresponding to the economic objective is expressed by the following formula:

[0022]

[0023] Where: Nt represents the scheduling period, which is generally 24 hours; represents the on-grid electricity price of the i-th new energy or new energy + energy storage combined station, represents the on-grid electricity price of the jth independent energy storage power station, represents the purchase price of electricity from the jth energy storage power;

[0024] The function corresponding to the volatility target is expressed by the following formula:

[0025]

[0026] Among them, Pav represents the average output value of the system in a unit period.

[0027] In some embodiments, the optimization decision module is further used to:

[0028] When receiving the active power target value, if the active power target value is greater than the current value, the internal adjustment of the new energy and electrochemical energy storage hybrid power station is started, and if the result of the adjustment does not reach the target value, the independent energy storage power station is triggered to discharge;

[0029] If the active power target value is lower than the set value, the internal regulation of the new energy and electrochemical energy storage hybrid power station and the charging instructions of the independent energy storage power station are triggered in turn;

[0030] When the grid frequency offset value is outside the dead zone of the primary frequency modulation action, determining whether the grid frequency is greater than a first threshold value,

[0031] When the grid frequency deviates from the primary frequency regulation dead zone, it enters the primary frequency regulation mode. If the frequency is higher than the preset threshold, the high-speed flywheel energy storage new energy station is called, and the internal regulation of the new energy and electrochemical energy storage hybrid power station and the charging of the independent energy storage power station are triggered in turn. When the power is fully charged, the power is abandoned.

[0032] If the frequency is lower than the preset threshold, the independent energy storage power station and the new energy and electrochemical energy storage hybrid power station are adjusted in turn to respond to the frequency regulation command, and the original mode is restored after the adjustment is completed.

[0033] In some embodiments, the data processing module obtains the operation data of the multi-source energy system in the multi-energy complementary area, including:

[0034] The wind power data is obtained by obtaining the operating conditions of the wind farm station in the multi-energy complementary area through the centralized control center, wherein the wind power data includes: AGC / AVC system data, primary frequency regulation system data, wind power monitoring system power data, wind power monitoring system status data, station meteorological observation data, ultra-short-term predicted power, short-term predicted power and medium- and long-term predicted power;

[0035] The operation conditions of the photovoltaic stations in the multi-energy complementary area are obtained through the centralized control center to obtain the photovoltaic data, wherein the photovoltaic data includes: AGC / AVC system data, primary frequency modulation system data, photovoltaic monitoring system power data, photovoltaic monitoring system status data, station meteorological observation data; ultra-short-term predicted power, short-term predicted power and medium- and long-term predicted power;

[0036] The operating conditions of the independent energy storage stations within the multi-energy complementary area are obtained through the centralized control center to obtain the independent energy storage data, wherein the independent energy storage data includes: AGC / AVC system data, primary frequency modulation system data, energy storage monitoring system power data and energy storage monitoring system status data;

[0037] The operating conditions of the new energy + energy storage combined stations within the jurisdiction are obtained through centralized control to obtain the new energy + energy storage combined data, wherein the new energy + energy storage combined data include: AGC / AVC system data, station monitoring system power data, station monitoring system status data, station meteorological observation data, ultra-short-term predicted power, short-term predicted power and medium- and long-term power prediction results, energy storage type and main functions.

[0038] In some embodiments, an output display interface and an operation status interface are generated based on the optimized scheduling scheme.

[0039] Among them, the output display interface displays the real-time output status of each station within the multi-energy complementary area, and / or the total resultant value obtained by combining the real-time output status of each station, and the operation status interface displays the operation curve of each station.

[0040] In a second aspect, an embodiment of the present application provides a multi-energy complementary coordinated control method based on multi-energy storage, the method comprising:

[0041] Obtaining the operating data of each power station within the multi-energy complementary area, wherein the operating data includes wind power data, photovoltaic data, independent energy storage data, and new energy + energy storage combined data;

[0042] The optimization target is obtained, and based on the optimization target, the operation data and the time scale information, a preset model is called to generate an optimization plan, and each power supply station within the multi-energy complementary area is regulated by the optimization plan.

[0043] In a third aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect above when executing the computer program.

[0044] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect above.

[0045] Compared with the related art, the multi-energy complementary coordinated control system based on multi-element energy storage provided in the embodiment of the present application obtains various data from multiple stations through data fusion to support optimized decision-making. Optimize the decision-making generation plan and display the operating status and benefit analysis to achieve precise control of the station. Model configuration management algorithm, setting boundary conditions and objective functions, etc. The internal coordination and supplementary logic plays a role in active power adjustment, grid frequency fluctuations and synchronous phase modulation. These means have improved the grid's acceptance of renewable energy, enabling the multi-energy complementary system to better respond to grid dispatch instructions and achieve optimal management of energy resources. At the same time, it enhances the stability and reliability of the grid, improves the economic benefits of the system, and provides a strong guarantee for the efficient operation of the multi-energy complementary coordinated control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0047] Figure 1 It is a structural block diagram of a multi-energy complementary coordinated control system based on multi-energy storage according to an embodiment of the present application;

[0048] Figure 2 is a schematic diagram of a processing display interface according to an embodiment of the present application;

[0049] Figure 3 is a schematic diagram of an optimization algorithm selection interface according to an embodiment of the present application;

[0050] Figure 4 is a structural schematic diagram of multi-energy complementary coordinated control based on multi-energy storage according to an embodiment of the present application;

[0051] Figure 5 is a flow chart of a multi-energy complementary coordinated control method based on multi-energy storage according to an embodiment of the present application;

[0052] Figure 6 It is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0054] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0055] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0056] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships, for example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0057] Wind energy and solar energy are not only complementary in time, but also in space. Compared with wind power or photovoltaic power generation alone, the wind-solar complementary power generation system can make the power output more stable, and can improve the grid's absorption and acceptance of renewable energy. Among the elements of the new power system construction, energy storage is a key technology to smooth out the randomness, intermittency and volatility of new energy, and further realize power supply reliability.

[0058] Renewable energy generation depends on natural conditions, such as wind instability and changes in sunlight, which makes its power output unstable. This unstable power output may cause instability in grid voltage, frequency and other parameters, increase the difficulty of grid operation and management, and reduce the safety and reliability of grid operation. In addition, when the grid accepts renewable energy generation, it is difficult to effectively dispatch and control it due to its large power fluctuations, which limits the large-scale application and efficient use of renewable energy in the grid.

[0059] In view of this, the embodiment of the present application provides a multi-energy complementary coordinated control system based on multi-energy storage. The multi-energy complementary coordinated control system is a bridge connecting the control center and various power stations within the jurisdiction, providing analysis and decision-making basis for the optimal control of wind power, photovoltaic power generation and energy storage, and optimizing the energy management form within the jurisdiction. The package includes three aspects: data fusion, optimal decision-making, and model configuration to meet the optimization needs between stations in the multi-energy complementary area. According to the information access characteristics of various energy sources (wind, light, storage) in the stations within the jurisdiction, real-time power and trend strategies are used to promote the flexible consumption of new energy.

[0060] Figure 1 is a structural block diagram of a multi-energy complementary coordinated control system based on multi-energy storage according to an embodiment of the present application, such as Figure 1 As shown, the system includes a data processing module 10, an optimization decision module 11 and a model configuration module 12, wherein:

[0061] The data processing module 10 is used to obtain the operation data of the multi-source energy system in the multi-energy complementary area, wherein the operation data includes wind power data, photovoltaic data, independent energy storage data and new energy + energy storage combined data;

[0062] The function of the data processing module 10 is to realize the integration of various data in the multi-energy complementary area, mainly obtaining some data from the station centralized control, power prediction system, and power market system, and providing a data basis for subsequent optimization decisions. Specifically,

[0063] Wind power: Obtain AGC / AVC system data of wind farms within the jurisdiction through centralized control; primary frequency regulation system data; wind power monitoring system power and status data; station meteorological observation data; obtain ultra-short-term, short-term and medium- and long-term power forecast results through power forecasting;

[0064] Photovoltaic: Obtain AGC / AVC system data of photovoltaic stations within the jurisdiction through centralized control; primary frequency regulation system data; photovoltaic monitoring system power and status data; station meteorological observation data; obtain ultra-short-term, short-term and medium- and long-term power forecast results through power forecasting;

[0065] Among them, the AGC system is mainly used to adjust the active power output of the power generation unit to maintain the stability of the power grid frequency and achieve real-time balance between power generation and load. Its data includes the actual active power output value of the power generation unit, the AGC command signal (that is, the expected power value issued by the power grid dispatching center based on the system frequency and load conditions), the adjustment rate (the speed at which the power generation unit responds to the AGC command to change the active power output), etc.

[0066] Primary frequency regulation refers to a frequency regulation method in which the power generation equipment automatically adjusts the active power output through its own speed regulation system to prevent further frequency changes when the grid frequency deviates from the rated value. The primary frequency regulation system data includes the active power change of the power generation unit when the frequency deviates, the frequency deviation value, the frequency regulation response time (the time interval from the frequency deviation to the start of the power generation unit's response), and the frequency regulation duration (the length of time the power generation unit continues to adjust the active power to restore the frequency).

[0067] Independent energy storage: Obtain AGC / AVC system data of energy storage stations within the jurisdiction through centralized control; primary frequency regulation system data; energy storage monitoring system power and status data;

[0068] New energy + energy storage joint station: obtain AGC / AVC system data of joint stations within the jurisdiction through centralized control; primary frequency regulation system data; power and status data of the new energy + energy storage joint station monitoring system; station meteorological observation data; obtain ultra-short-term, short-term and medium- and long-term power forecast results through power forecasting; basic information such as energy storage type and main functions of new energy + energy storage joint stations.

[0069] The optimization decision module 11 is used to obtain the optimization target, and based on the optimization target, operation data and time scale information, call the preset model through the model configuration module 12 to generate an optimization scheduling plan, and optimize and regulate the stations within the multi-energy complementary area through the optimization scheduling plan.

[0070] Among them, the optimization decision module 11 first obtains various data from the station operation from the data processing module 10, and then uses the optimization calculation method to process these data, and presents the processing results in the form of intuitive graphs, tables, texts, etc.; its purpose is to achieve comprehensive optimization and regulation of the stations within the jurisdiction of the multi-energy complementary system. The specific operation generates an adaptive optimization plan based on the currently set combination mode and optimization target.

[0071] Specifically, the optimization module relies on the parameters and rules determined by the model configuration module 12. Key information such as the current combined operation mode (e.g., different combinations and collaborative working modes between wind power, photovoltaics, and energy storage), the current goals to be achieved (e.g., pursuing optimal economic performance, minimizing volatility, etc.), and the time scale (short-term, medium-term, or long-term planning) are all set and confirmed in detail in the model configuration module 12. This information constitutes the basic framework for the generation of the optimization plan and clarifies the direction and focus of the optimization.

[0072] For example, if the current goal is set as economic optimization and the time scale is short-term (such as one day) in the model configuration, the optimization decision module 11 will focus on how to minimize costs and maximize benefits by reasonably arranging the output of each station within the day in subsequent calculations based on this.

[0073] Furthermore, based on the settings of the model configuration module 12, the optimization decision module 11 uses the built-in optimization algorithm and model to deeply analyze and calculate the station operation data, thereby generating specific optimization plans. These plans not only include the output plans of each station at different time points, but also involve the charging and discharging strategies of the energy storage system, the adjustment of the operating parameters of the energy conversion equipment, and other aspects, so as to achieve the optimal operating state of the entire multi-energy complementary system under given conditions.

[0074] In addition, the optimization decision module 11 can also display the generated control scheme results to the system operators in an intuitive and comprehensive manner. It defaults to display the detailed output scheme of each station within the jurisdiction from 0 to 24 hours on the previous day after complex optimization calculations, presents the planned output curve of each station at different times of the day in the form of a chart, and lists key data points (such as the expected output value at each hour) in a table, and explains the basic assumptions, optimization goals and main constraints of the scheme in text, so that the operators can quickly obtain the core content and expected results of the optimization scheme.

[0075] Taking into account the impact of various uncertain factors (such as weather changes, load fluctuations, etc.) on the station output in actual operation, the optimization decision module 11 has real-time dynamic adjustment capabilities. The daily operation instructions are not static, but can be flexibly adjusted according to real-time power monitoring data. By comparing the actual power with the planned output in real time, when it is found that the deviation exceeds the allowable range, the system automatically triggers the recalculation and adjustment mechanism, and promptly corrects the output instructions of each station to ensure that the system always runs towards the optimization goal, while maintaining the ability to respond quickly to changes in the power grid, and maintaining the balance of supply and demand and stable operation of the power grid.

[0076] Furthermore, the optimization decision module 11 also provides an output display interface. Figure 2 is a schematic diagram of a processing display interface according to an embodiment of the present application, such as Figure 2As shown, this interface provides system operators with a window for real-time monitoring of the operating status of each station. It can clearly present the real-time output of each station in the jurisdiction in the form of a dynamic chart, and compare it with the pre-set target values ​​of each station.

[0077] Finally, the text on the right side of the operation status function panel displays the current output value of the station, which is convenient for quickly understanding the instant power generation / power supply capacity of each station. If there are assessment indicators, the fluctuation rate of the combined output curve will also be displayed to reflect the stability of the system output power, which helps to find problems and make adjustments in time. The interface displays the daily operation curve from 0-24 hours by default, showing the trend of the system's power generation output throughout the day, including the output changes and complementary processes of different energy sources, which is conducive to analyzing operation rules, predicting status, evaluating performance and optimizing strategies.

[0078] The benefit analysis module can screen and display multiple indicators of each station and multi-energy complementary system in the same time period (such as a certain period in the past), such as power generation efficiency, energy utilization rate, etc. By comparing indicators under the same combination mode in different periods, the system performance changes and optimization effects can be evaluated, and problems and improvement directions can be found.

[0079] In addition, the daily energy storage revenue (including peak-valley electricity price difference arbitrage, ancillary service revenue, etc.) and transaction revenue (revenue from electricity sales, capacity trading, demand response trading, etc.) under the current combination are counted. The accumulation of these data can help understand the economic performance of the system, evaluate profitability and return on investment, provide a basis for operation and investment decisions, and can also be used to compare with other systems to improve competitiveness.

[0080] In this embodiment, the model configuration module 12 is responsible for managing the models and algorithms required by the optimization decision module 11, and can perform call, deployment and verification functions. At the same time, basic management of the stations within the jurisdiction of the multi-energy complementary system is performed, including determining the combination mode, optimizing boundary conditions, selecting optimization algorithms and goals, etc., and the solution results are displayed in the interface of the optimization decision module 11.

[0081] Specifically, the model configuration module 12 is used to:

[0082] (1) Station management

[0083] Manage the basic information of each station, such as "project number", "full name of power station", "affiliated unit", "power station type", "grid connection time", "access time", "province", "city", etc., to provide basic data support for the system to accurately identify and manage stations.

[0084] (2) Operation management

[0085] The model and index characteristic management of the station control objects belonging to multi-energy complementarity, including object model, boundary conditions, index characteristics, objective function and optimization algorithm; among which, the control object models include: photovoltaic, wind power, energy storage (electrochemical energy storage, flywheel energy storage);

[0086] 2) Boundary conditions:

[0087] External environmental factors: Setting electricity prices Electricity purchase price As well as auxiliary market transaction principles, etc.; set as adjustable items as the basic parameters of the optimization algorithm. It can be understood that the setting of parameters such as the power selling price φs, the power purchasing price φb and the auxiliary market transaction principles enables the system to closely integrate with the power market environment for optimized operation. The power selling price and the power purchasing price directly affect the economic benefits of the system. When formulating power generation plans and energy storage strategies, the system will adjust energy production and consumption behaviors according to these price signals.

[0088] Internal coordination characteristics influencing factors:

[0089]

[0090] Where: Pload,t represents the output demand at time t, Pi,t represents the output power of the i-th new energy and joint station at time t. Pj,t represents the interactive power of the j-th energy storage station at time t, with output being positive and input being negative.

[0091]

[0092] Where: Pimax represents the predicted maximum output plan of the i-th new energy station.

[0093]

[0094] Where: P1j,max represents the maximum value of the energy storage charging power in the tth period, and P2j,max represents the maximum value of the energy storage charging power in the tth period.

[0095] Through the above means, a series of equality and inequality constraints are used to ensure the power coordination and balance between the stations within the system and between the stations and the energy storage system.

[0096] In this embodiment, economy and / or volatility are used as control objectives. Under a single objective function, a suitable objective function can be selected based on the assessment demand. Under a multi-objective function, the objective function is determined by combining the following two formulas:

[0097] Best economic performance:

[0098]

[0099] Where: Nt represents the scheduling period, which is generally 24 hours; Represents the on-grid electricity price of the i-th new energy and joint station, Yuan / kWh. represents the on-grid electricity price of the j-th independent energy storage power station, yuan / kWh. Represents the purchase price of the j-th energy storage electricity, yuan / kWh.

[0100] By maximizing this objective function, the system can seek the optimal power generation and energy storage scheduling strategy to maximize economic benefits while meeting the energy supply and system operation constraints. For example, in different electricity price periods, the system will reasonably arrange new energy power generation, energy storage charging and discharging, and energy exchange with the power grid according to the costs and benefits of each station, thereby improving the overall profitability of the system.

[0101] Optimal volatility:

[0102]

[0103] Where: Pav represents the average output value of the system in a unit cycle;

[0104] It can be understood that in this embodiment, by minimizing the volatility objective function, the system can adjust the output strategy of each station, especially by utilizing the rapid response characteristics of the energy storage system to effectively smooth the intermittency and volatility of renewable energy power generation, improve the stability of the system output power, and enhance the system's friendliness and compatibility with the power grid.

[0105] After determining the objective function, this system provides a variety of intelligent optimization algorithms. Figure 3 is a schematic diagram of an optimization algorithm selection interface according to an embodiment of the present application, such as Figure 3 As shown in the figure, intelligent algorithms such as evolutionary algorithms, particle swarm optimization algorithms, ant colony optimization algorithms, etc., and these algorithms are fully managed. By setting information such as "algorithm name", "algorithm program code", "algorithm type", and "algorithm description", different algorithms can be clearly identified and called, and the appropriate optimization algorithm can be flexibly selected according to actual needs. These intelligent optimization algorithms have global search capabilities and adaptive adjustment capabilities, and can find optimal solutions or approximate optimal solutions in complex multi-variable and multi-constrained optimization problems.

[0106] For example, evolutionary algorithms simulate the inheritance, mutation, and selection mechanisms in the biological evolution process, and gradually approach the optimal solution by continuously iterating and optimizing individuals in the population (i.e., different combinations of operating strategies);

[0107] In this embodiment, the system selects a suitable optimization algorithm to solve the model according to different optimization objectives, site characteristics and operating conditions, so as to obtain the optimal operating plan that meets the system requirements, and passes the solution result to the optimization decision module 11 to guide the actual operation and regulation of the system.

[0108] In addition, the system also provides an internal coordination and supplement module, which belongs to the optimization decision module 11. The optimization decision module 11 is mainly responsible for calculating and analyzing the acquired data, and generating optimization plans to regulate the various stations in the system. The internal coordination and supplement logic clarifies how to coordinate the work between various energy sources and energy storage units under different operating conditions. It is a key logical component for achieving precise regulation in the optimization decision module 11.

[0109] In terms of active power regulation, this internal coordination and supplementary module can ensure that the system active power meets the requirements of the power grid; by comparing the active target value with the current active power, it can reasonably trigger the adjustment instructions of the new energy + electrochemical energy storage hybrid power station and the independent energy storage power station to achieve precise adjustment of the active power.

[0110] For example, when the increase in electricity demand causes the active power target to rise, the regulation capacity of the new energy + electrochemical energy storage hybrid power station is used first. If it is still insufficient, the independent energy storage power station is started to discharge and supplement power. On the contrary, when there is excess active power, the power stations are adjusted in an orderly manner to carry out energy storage charging or reduce power generation to maintain system power balance, improve energy utilization efficiency, and ensure stable operation of the power grid.

[0111] Furthermore, when the grid frequency fluctuates (shifts outside the primary frequency regulation dead zone), the multi-energy complementary system can respond quickly and enter the primary frequency regulation mode to enhance the grid frequency stability. Different frequency regulation resource calling sequences and adjustment strategies are formulated according to different situations where the grid frequency is too high or too low.

[0112] When the frequency is high, high-speed flywheel energy storage is used first to quickly absorb excess energy, and then the power is further adjusted by combining the internal regulation of new energy + electrochemical energy storage and the charging of independent energy storage power stations; when the frequency is low, the active power output is increased in the order of independent energy storage power station discharge and new energy + electrochemical energy storage internal regulation. When the power support of a frequency regulation ends, the system can automatically switch back to the original working mode to ensure the stable transition and continuous operation of the system under different frequency states, effectively reducing the impact of frequency deviation on the power grid and users.

[0113] Finally, for the inertial flywheel energy storage that undertakes the synchronous phase modulation instruction, in this embodiment, monitoring rather than direct calling is adopted, which can not only ensure its normal operation to stabilize the voltage phase and meet the power grid's requirements for power quality, but also avoid unnecessary operational interference, ensure the stability and reliability of the entire multi-energy complementary system during the synchronous phase modulation process, maintain the quality and phase balance of the grid voltage, and ensure the safe and stable operation of the power system.

[0114] also, Figure 4 It is a structural schematic diagram of multi-energy complementary coordinated control based on multi-energy storage according to an embodiment of the present application.

[0115] This system obtains various data from multiple stations through data fusion to support optimization decisions. The optimization decision-making generates solutions and displays the operating status and benefit analysis to achieve precise control of the station. The model configures the management algorithm, sets boundary conditions and objective functions, etc. The internal coordination and supplementary logic plays a role in active power adjustment, grid frequency fluctuations and synchronous phase modulation. These means have improved the grid's acceptance of renewable energy, enabling the multi-energy complementary system to better respond to grid dispatching instructions and achieve optimal management of energy resources. At the same time, it has enhanced the stability and reliability of the grid, improved the economic benefits of the system, and provided a strong guarantee for the efficient operation of the multi-energy complementary coordinated control system.

[0116] On the other hand, the embodiment of the present application also provides a multi-energy complementary coordinated control method based on multi-energy storage, Figure 5 is a flow chart of a multi-energy complementary coordinated control method based on multi-energy storage according to an embodiment of the present application, such as Figure 5 As shown, the method includes the following process:

[0117] S501, obtaining the operation data of each power station in the multi-energy complementary area, wherein the operation data includes wind power data, photovoltaic data, independent energy storage data and new energy + energy storage combined data;

[0118] S502, obtaining the optimization target, and based on the optimization target, operation data and time scale information, calling the preset model to generate an optimization plan, and regulating each power supply station within the multi-energy complementary area through the optimization plan.

[0119] Through the above steps S501 to S502, various data from multiple stations are obtained through data fusion to support optimization decisions. The optimization decision-making plan is generated and the operation status and benefit analysis are displayed to achieve precise control of the station. The model configuration management algorithm, setting boundary conditions and objective functions, etc. The internal coordination and supplementary logic plays a role in active power adjustment, grid frequency fluctuations and synchronous phase modulation. These means have improved the grid's acceptance of renewable energy, enabling the multi-energy complementary system to better respond to grid dispatching instructions and achieve optimal management of energy resources. At the same time, the grid stability and reliability are enhanced, the system economic benefits are improved, and a strong guarantee is provided for the efficient operation of the multi-energy complementary coordinated control system.

[0120] In one embodiment, Figure 6 is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application, such as Figure 6 As shown, an electronic device is provided, which may be a server, and its internal structure diagram may be as shown in Figure 6 As shown. The electronic device includes a processor, a network interface, an internal memory and a non-volatile memory connected through an internal bus, wherein the non-volatile memory stores an operating system, a computer program and a database. The processor is used to provide computing and control capabilities, the network interface is used to communicate with an external terminal through a network connection, the internal memory is used to provide an environment for the operation of the operating system, the computer program is executed by the processor to implement a multi-energy complementary coordinated control method based on multi-energy storage, and the database is used to store data.

[0121] Those skilled in the art will understand that Figure 6 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0122] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by hardware related to computer program instructions. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0123] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0124] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A multi-energy complementary coordinated control system based on multi-element energy storage, characterized in that: The system includes: a data processing module, an optimization decision module and a model configuration module, wherein: The data processing module is used to obtain the operating data of each power station in the multi-energy complementary area, wherein the operating data includes wind power data, photovoltaic data, independent energy storage data and new energy + energy storage combined data; The optimization decision module is used to obtain the optimization target, and based on the optimization target, the operating data and the time scale information, call the preset model through the model configuration module to generate an optimization plan, and regulate each power supply station within the multi-energy complementary area through the optimization plan.

2. The system according to claim 1, characterized in that The model configuration module includes a station management module and an operation management module, wherein: The station management module is used to manage the basic information of each station within the multi-energy complementary area, wherein the basic information includes: project number, power station name, affiliated unit, power station type, grid connection time, access time and area; The operation management module is used to manage the control objects and index characteristics in each station within the multi-energy complementary area, wherein the control objects include photovoltaic object models, wind power object models and energy storage models, and the index characteristics include: boundary conditions, index characteristics, objective functions and optimization algorithms.

3. The system according to claim 2, characterized in that The boundary conditions include: external environmental influencing factors and internal coordination characteristic influencing factors, wherein the external environmental influencing factors include: electricity receiving price, electricity purchasing price and auxiliary market transaction principles; The internal coordination characteristic influencing factors are expressed by the following formula: Among them: Pload,t represents the output demand at time t, Pi,t represents the output power of the i-th renewable energy and renewable energy + energy storage combined station at time t, Pj,t represents the interactive power of the j-th energy storage station at time t, output is positive and input is negative; 0≤P i (t)≤P i max Among them, Pimax represents the predicted maximum output plan of the i-th new energy station; Among them, P1j,max represents the maximum value of the energy storage charging power in the tth period, and P2j,max represents the maximum value of the energy storage charging power in the tth period.

4. The system according to claim 2, characterized in that The objective function is determined based on an economic objective and / or a volatility objective, wherein: The function corresponding to the economic objective is expressed by the following formula: Where: Nt represents the scheduling period; represents the on-grid electricity price of the i-th new energy or new energy + energy storage combined station, represents the on-grid electricity price of the jth independent energy storage power station, represents the purchase price of electricity from the jth energy storage power; The function corresponding to the volatility target is expressed by the following formula: Among them, Pav represents the average output value of the system in a unit period.

5. The system according to claim 1, characterized in that The optimization decision module is also used for: When receiving the active power target value, if the active power target value is greater than the current value, the internal adjustment of the new energy and electrochemical energy storage hybrid power station is started, and if the result of the adjustment does not reach the target value, the independent energy storage power station is triggered to discharge; If the active power target value is lower than the set value, the internal regulation of the new energy and electrochemical energy storage hybrid power station and the charging instructions of the independent energy storage power station are triggered in turn; When the grid frequency offset value is outside the dead zone of the primary frequency modulation action, determining whether the grid frequency is greater than a first threshold value, When the grid frequency deviates from the primary frequency regulation dead zone, it enters the primary frequency regulation mode. If the frequency is higher than the preset threshold, the high-speed flywheel energy storage new energy station is called, and the internal regulation of the new energy and electrochemical energy storage hybrid power station and the charging of the independent energy storage power station are triggered in turn. When the power is fully charged, the power is abandoned. If the frequency is lower than the preset threshold, the independent energy storage power station and the new energy and electrochemical energy storage hybrid power station are adjusted in turn to respond to the frequency regulation command, and the original mode is restored after the adjustment is completed.

6. The system according to claim 1, characterized in that The data processing module obtains the operation data of the multi-source energy system in the multi-energy complementary area, including: The wind power data is obtained by obtaining the operating conditions of the wind farm station in the multi-energy complementary area through the centralized control center, wherein the wind power data includes: AGC / AVC system data, primary frequency regulation system data, wind power monitoring system power data, wind power monitoring system status data, station meteorological observation data, ultra-short-term predicted power, short-term predicted power and medium- and long-term predicted power; The operation conditions of the photovoltaic stations in the multi-energy complementary area are obtained through the centralized control center to obtain the photovoltaic data, wherein the photovoltaic data includes: AGC / AVC system data, primary frequency modulation system data, photovoltaic monitoring system power data, photovoltaic monitoring system status data, station meteorological observation data; ultra-short-term predicted power, short-term predicted power and medium- and long-term predicted power; The operating conditions of the independent energy storage stations within the multi-energy complementary area are obtained through the centralized control center to obtain the independent energy storage data, wherein the independent energy storage data includes: AGC / AVC system data, primary frequency modulation system data, energy storage monitoring system power data and energy storage monitoring system status data; The operating conditions of the new energy + energy storage combined stations within the jurisdiction are obtained through centralized control to obtain the new energy + energy storage combined data, where the data include: AGC / AVC system data, station monitoring system power data, station monitoring system status data, station meteorological observation data, ultra-short-term predicted power, short-term predicted power and medium- and long-term power prediction results, energy storage type and main functions.

7. The system according to claim 1, characterized in that Generate an output display interface and an operation status interface based on the optimized scheduling scheme, Among them, the output display interface displays the real-time output status of each station within the multi-energy complementary area, and / or the total resultant value obtained by combining the real-time output status of each station, and the operation status interface displays the operation curve of each station.

8. A multi-energy complementary coordinated control method based on multi-energy storage, characterized in that: The method comprises: Obtaining the operating data of each power station within the multi-energy complementary area, wherein the operating data includes wind power data, photovoltaic data, independent energy storage data, and new energy + energy storage combined data; The optimization target is obtained, and based on the optimization target, the operation data and the time scale information, a preset model is called to generate an optimization plan, and each power supply station within the multi-energy complementary area is regulated by the optimization plan.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to claim 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to claim 8 is implemented.

Images