A comprehensive energy collaborative management and control system

Abstract

This invention belongs to the field of electrical technology and discloses a comprehensive energy collaborative management and control system. The system includes: an optimization scheduling layer, comprising a cloud platform, an energy management system, a digital twin system, a user-side virtual power plant, and an external interaction system; a coordination control layer, comprising a central coordination control system, a regional coordination controller, a grid connection interface device, and a thermal coordination control system, wherein the regional coordination controller is communicatively connected to the energy management system; and a sensing and self-control layer, which is communicatively connected to the regional coordination controller and sends data acquired by the sensing and self-control layer to the regional coordination controller, so that the regional coordination controller can formulate and output collaborative control strategies based on the data acquired by the sensing and self-control layer. This invention solves the technical problem of poor resource efficiency caused by the inability of existing network architectures to achieve comprehensive energy management.

Description

Technical Field

[0001] This invention relates to the field of electrical technology, and in particular to an integrated energy coordination and management system. Background Technology

[0002] At present, in order to solve the problem of traditional energy shortages, the utilization of new energy sources and the complementary technologies of multiple energy sources are becoming increasingly widespread. However, in the field of integrated energy, there are significant differences between various energy systems, which poses a huge challenge to multi-energy complementary systems.

[0003] Therefore, how to fully utilize the complementary characteristics of various energy sources, establish a collaborative management and control framework for various heterogeneous energy sources, explore energy efficiency improvement resources in all aspects such as source-grid-load-storage, and enhance the absorption capacity of renewable energy has become an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0004] This invention provides an integrated energy collaborative management and control system to at least partially solve the technical problem of poor resource efficiency caused by the inability of existing network architectures to achieve individual energy management. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0005] According to a first aspect of the present invention, an integrated energy collaborative management and control system is provided, the system comprising:

[0006] The optimized scheduling layer includes a cloud platform, an energy management system, a digital twin system, a user-side virtual power plant, and an external interaction system.

[0007] The coordination control layer includes a central coordination control system, regional coordination controllers, grid connection interface devices, and a thermal coordination control system, wherein the regional coordination controllers are communicatively connected to the energy management system.

[0008] The sensing and control layer is communicatively connected to the regional coordination controller and sends the data acquired by the sensing and control layer to the regional coordination controller so that the regional coordination controller can formulate and output a cooperative control strategy based on the data acquired by the sensing and control layer.

[0009] Furthermore, the sensing and control layer includes a microgrid fault recorder, an energy controller, a local control system, a power distribution IoT terminal, meters, sensors, and a gateway;

[0010] The microgrid fault recorder, energy controller, local control system, distribution IoT terminal, meter, sensor and gateway will transmit the collected data to the regional coordination controller.

[0011] Furthermore, the regional coordination controller includes a multi-level functional application layer.

[0012] Furthermore, the functional application layer specifically includes at least one of the following: the advanced application layer, the basic application layer, the platform support layer, and the platform service layer.

[0013] Furthermore, the advanced application layer includes at least one of the following: an operation control module, a comprehensive evaluation module, an optimized scheduling module, a microgrid control module, a power grid support module, and a transaction auxiliary decision-making module.

[0014] Furthermore, the basic application layer includes at least one of the following: a data service module, a comprehensive monitoring module, a multi-functional prediction module, and an operational indicator monitoring module.

[0015] Furthermore, the data service module is used to receive massive amounts of data acquired by the perception and control layer, and to clean the massive amounts of data to obtain an ordered dataset.

[0016] Furthermore, the data service module is also used to intercept at least one of the broadcast message, point-to-point message reception status, and point-to-point message transmission status during the data transmission process, and to monitor the data transmission process based on the intercepted data.

[0017] Furthermore, the data service module is also used to acquire network traffic and system resource traffic, and to monitor the data transmission process based on the network traffic and system resource traffic.

[0018] Furthermore, the integrated monitoring module is used to output the monitoring results and to output alarm information when the monitoring results are abnormal.

[0019] Furthermore, the multi-energy prediction module is used to predict the power generation of each energy system and output the prediction results.

[0020] Furthermore, the operation control module is used to jointly schedule controllable resources, wherein the controllable resources include electrical energy, cooling, heating and gas resources.

[0021] Furthermore, the comprehensive evaluation module is used to evaluate the overall adjustability of the region through adjustment methods, control time, and response methods.

[0022] Furthermore, the optimization scheduling module is used to acquire optimization instructions and control strategies, and issue instructions to the user-side terminal system based on the control strategies.

[0023] Furthermore, the control strategy is issued collaboratively through the energy management system, the regional coordination controller, and the local automatic control system.

[0024] Furthermore, the power grid support module is used to issue a response command when an accident is confirmed.

[0025] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0026] The integrated energy collaborative management and control system provided by this invention fully utilizes the complementary characteristics of various energy sources, establishes a collaborative management and control architecture for various heterogeneous energy sources, and explores energy efficiency improvement resources in all aspects such as source-grid-load-storage, thereby enhancing the absorption capacity of renewable energy. It realizes the complementarity and mutual assistance among various energy sources, joint scheduling, and the digitization and visualization of energy consumption data throughout the planned area, improving the efficient utilization of integrated energy and reliable power supply. This solves the technical problem of poor resource efficiency caused by the inability of existing network architectures to achieve comprehensive energy management and control.

[0027] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0028] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0029] Figure 1 A structural block diagram of a specific embodiment of the integrated energy collaborative management and control system provided by the present invention;

[0030] Figure 2 for Figure 1 The diagram shows the structural block diagram of the software architecture in the integrated energy collaborative management and control system. Detailed Implementation

[0031] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some embodiments may include or substitute parts and features of other embodiments. The scope of the embodiments herein encompasses the entire scope of the claims and all available equivalents thereof. Throughout this document, the terms “first,” “second,” etc., are used only to distinguish one element from another without requiring or implying any actual relationship or order between the elements. Indeed, a first element can also be referred to as a second element, and vice versa. Furthermore, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a structure, apparatus, or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the structure, apparatus, or device that includes said element. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0032] The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer" used in this document to indicate orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings. They are used solely for the convenience of describing the document and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In the description herein, unless otherwise specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two elements; they can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0033] In this document, unless otherwise stated, the term "multiple" means two or more.

[0034] In this article, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0035] In this article, the term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0036] Please refer to Figure 1 , Figure 1 This is a structural block diagram of a specific embodiment of the integrated energy collaborative management and control system provided by the present invention.

[0037] In one specific embodiment, the present invention provides an integrated energy collaborative management and control system, in which the overall architecture adopts a hierarchical and partitioned, coordinated control mode. The hierarchical structure is roughly divided into three layers from top to bottom: the optimization scheduling layer, the coordination control layer, and the sensing and self-control layer. The partitioned structure is divided into three areas: the production control area, the information management area, and the Internet interaction area.

[0038] The optimized scheduling layer is used for regional optimized scheduling, and includes a cloud platform, energy management system, digital twin system, user-side virtual power plant and external interaction system; the optimized scheduling layer has left and right communication and control hubs to realize the integrated energy optimization scheduling of multi-energy complementarity, big data integrated visualization display and efficient interactive experience of the entire region.

[0039] The coordination and control layer includes a central coordination and control system, regional coordination controllers, grid connection interface devices, and a thermal coordination and control system. The regional coordination controllers are communicatively connected to the energy management system. Specifically, the regional coordination controllers communicate with the energy management system and access local sensing and control layer data via MODEBUS to issue coordination strategies. Based on GOOSE, they can independently achieve millisecond-level control of independent power supply to critical loads. Specific functions include: microgrid routine operation control, tie-line power control, microgrid grid-to-grid switching, and distributed energy station unit monitoring. It can also isolate the microgrid from the main grid during grid failures; isolate faulty areas within the microgrid; and achieve transient stability control during grid-to-grid switching and off-grid operation. The regional coordination controllers execute control commands from the regional coordination and control system, issuing them to local sensing and control layer devices to complete relevant operations, thus realizing the microgrid's operation control functions.

[0040] The sensing and control layer is communicatively connected to the regional coordination controller and sends the data acquired by the sensing and control layer to the regional coordination controller so that the regional coordination controller can formulate and output a cooperative control strategy based on the data acquired by the sensing and control layer.

[0041] Specifically, the sensing and control layer includes a microgrid fault recorder, an energy controller, a local control system, a power distribution IoT terminal, meters, sensors, and a gateway; the microgrid fault recorder, energy controller, local control system, power distribution IoT terminal, meters, sensors, and gateway transmit the collected data to the regional coordination controller.

[0042] The sensing and control layer enables flexible access to the underlying device layer, supporting communication methods such as fiber optic, Ethernet, 5G, LoRa, and HPLC. For example, the local control system accesses local device data via MODEBUS and connects to the thermal coordination control system via Ethernet port and TCP / IP protocol (network cable or fiber optic cable). The thermal coordination control system communicates with the energy management system via the 104 protocol.

[0043] Furthermore, the software architecture of the integrated energy collaborative management and control system comprehensively considers external data characteristics such as multi-level architecture, multiple business components, multiple types of energy, multiple data types, multiple access protocols, multiple types of user access, multiple data publishing methods, and multiple external interfaces. It provides a unified data acquisition framework and security system, which can not only realize standardized data access, but also fully guarantee the integrity, reliability, availability, controllability, and confidentiality of energy facilities, energy systems, and energy information.

[0044] Accordingly, the regional coordination controller includes a multi-level functional application layer, such as... Figure 2 As shown, the functional application layer specifically includes at least one of the following: advanced application layer, basic application layer, platform support layer, and platform service layer.

[0045] The advanced application layer includes at least one of the following: an operation control module, a comprehensive evaluation module, an optimized scheduling module, a microgrid control module, a power grid support module, and a transaction auxiliary decision-making module. The basic application layer includes at least one of the following: a data service module, a comprehensive monitoring module, a multi-energy prediction module, and an operation indicator monitoring module. The platform support layer mainly includes an integrated model center and a full-service data center. The platform service layer includes database services, a human-machine interface, alarm services, access control, topology analysis, data interfaces, and publishing / browsing capabilities.

[0046] In the basic application layer, the data service module receives massive amounts of data acquired by the perception and control layer and cleans this data to obtain an ordered dataset. Data cleaning solves data quality problems and makes the data more suitable for mining. The result of data cleaning is the corresponding processing of various messy data to obtain standardized, high-quality, and continuous data, providing a data source for big data statistics and big data mining.

[0047] Furthermore, the data service module is also used to intercept at least one of the broadcast message, point-to-point message reception status, and point-to-point message transmission status during the data transmission process, and to monitor the data transmission process based on the intercepted data. The data service module is also used to acquire network traffic and system resource traffic, and to monitor the data transmission process based on the network traffic and system resource traffic.

[0048] In other words, data monitoring listens to the transmission status of broadcast messages and point-to-point messages during data transmission, and also monitors the network traffic and system resource status caused by data transmission.

[0049] The aforementioned integrated monitoring module outputs monitoring results and alarm information when abnormalities occur. Specifically, the integrated monitoring module enables intelligent online operation monitoring, which provides hierarchical online monitoring of operational status from system-level data to various regions, multi-layered nested microgrids, and underlying devices. Through long-term accumulation, a complete operational database is formed, providing efficient background data processing, data storage and retrieval, data synchronization, and data backup services for data analysis and report generation. The intelligent online operation monitoring system has the ability to monitor various types of devices in real time, displaying information through geographic maps, electrical main wiring diagrams, dedicated graphic elements, line graphs, bar charts, pie charts, lists, and other methods. It provides integrated graphical operation, viewing and management of various information parameters, and facilitates quick querying of statistical information. It monitors the operation of all network devices and, based on device operating parameters and real-time data, issues real-time alarms for abnormal device operation states. Alarm content includes, but is not limited to, line overload, transformer overload, bus voltage exceeding limits, and distributed power source anomalies. Simultaneously, the system monitors the operation status of the regions and microgrids within its jurisdiction in real time and issues alarms for abnormal operation of regions and microgrids, such as abnormal microgrid disconnection or system frequency exceeding limits.

[0050] Furthermore, this integrated monitoring module also features statistical analysis capabilities. Based on real-time monitoring data, this function performs real-time calculations and historical statistics on the operation of distributed energy resources and loads within the system. The statistical analysis functions include: statistical analysis of the installed capacity, power generation, utilization hours, and blocked power of distributed energy resources; statistical analysis of environmental meteorological data; statistical analysis of output characteristics (maximum output, minimum output, simultaneity rate, etc.); and statistical analysis of the correlation between distributed energy resources and loads. The results of the statistical analysis are displayed using tables, curves, pie charts, radar charts, and other methods.

[0051] The aforementioned multi-energy forecasting module is used to predict the power generation of various energy systems and output the prediction results. This module enables accurate prediction of new energy power generation, facilitating the coordinated operation of conventional and new energy power generation within the integrated energy system. It allows for timely adjustments to dispatch plans, reducing the impact of distributed new energy on the power grid and improving the safety and stability of grid operation. The multi-energy forecasting module can perform predictions for various scenarios and functions. For example, power generation prediction uses historical operational data such as the installed capacity of new energy sources like wind and solar power, meteorological information (temperature, wind speed, and solar irradiance), and power generation to determine the relationship between power generation and various related factors, thus scientifically predicting the power generation curve. Load forecasting is a crucial daily task for power system dispatching and operation departments and electricity service providers. Its accuracy directly impacts the safety, economy, and power supply quality of the power system. Energy consumption forecasting mainly involves systems such as central air conditioning and thermal storage, including power load forecasting, heat load forecasting, and cooling load forecasting. Based on the energy consumption characteristics of different types of loads and historical load data, and taking into account factors such as season, weather, human behavior, special holidays, and user demand, a typical user load curve is formed to predict the load baseline for the next few days. After the regulation begins, there will be an error between the baseline and the actual load. The baseline is corrected in real time through correction methods.

[0052] The aforementioned operation control module is used for the joint scheduling of controllable resources, including electrical energy, cooling, heating, and gas resources. It should be understood that operational indicators refer to the basic data indicators of system operation. For example, the operational indicators of an energy management system are divided into several basic statistical indicator systems, such as clean and low-carbon, safe and efficient, economical and high-quality, and cooling, heating, electricity, and storage. The clean and low-carbon indicator system includes indicators such as energy conservation and emission reduction, local consumption rate of new energy, and the proportion of clean energy in the region. The safe and efficient indicator system includes indicators such as voltage qualification rate, line load rate, N-1 pass rate of key equipment, comprehensive energy efficiency ratio, and power supply efficiency. The economical and high-quality indicator system includes indicators such as clean energy power generation, regional power reception, power supply reliability, and availability of key equipment. The cooling, heating, electricity, and storage indicator system includes indicators such as cumulative cooling capacity, cumulative heating capacity, cumulative power generation, cumulative grid-connected power, cumulative power reception, cumulative power consumption, cumulative energy storage charging capacity, and cumulative energy storage discharging capacity.

[0053] At the advanced application layer, the comprehensive evaluation module evaluates the overall regional adjustability through adjustment methods, control timing, and response methods. The optimization scheduling module acquires optimization instructions and control strategies, and issues instructions to the user-side terminal system based on the control strategies. The control strategies are issued collaboratively through the energy management system, regional coordination controller, and local coordination devices at the sensing layer. The power grid support module issues response instructions upon confirmation of an accident.

[0054] More specifically, for integrated energy systems, the core of optimized operation decisions in steady-state control scenarios is the joint scheduling of controllable resources throughout the system. Controllable resources include not only conventional electrical energy but also resources such as cooling, heating, and gas, requiring coordinated and optimized scheduling of information from these multiple energy resources. Based on different control objective functions, this can be categorized into safety mode, green mode, and economic mode.

[0055] The objectives of the safe mode operation are as follows: During peak electricity consumption, some power distribution equipment or lines may be under heavy load or even overload, causing the power distribution network to be in an abnormal state. Measures need to be taken to restore it to a normal state and to attempt optimization to bring it into a high-efficiency operating state. Through analysis of the power supply system's operating status and potential operational risks, early warning and preventative control are implemented to improve the power supply system's safety margin, enhance the grid's ability to withstand risks, and ensure the system's safe and stable operation. The objectives of the green mode operation are: Through analysis of the power supply system's operating status and potential operational risks, to maximize the absorption of clean energy within the region, achieving 100% clean energy output, preventing wind / solar curtailment, realizing on-site absorption of clean energy, increasing the proportion and utilization rate of clean energy, utilizing the time-series energy regulation function of energy storage to improve the greenness of electricity consumption, increase overall energy utilization efficiency, reduce the proportion of electricity, and ensure the green operation of the system.

[0056] The goal of economic model optimization is to minimize system operating costs while ensuring the safe and stable operation of the system. This involves reducing system electricity costs, primarily considering the following constraints: optimizing the power supply modes of DC power supply to DC load and AC power supply to AC load to reduce energy conversion links between AC / DC power generation and consumption systems, thereby improving power supply efficiency; reducing the number of lines and line losses by constructing a DC low-voltage distribution system; reducing power transmission on feeders and improving energy efficiency by realizing local consumption of distributed power sources; and coordinating the charging and discharging of energy storage and the use of flexible loads through peak-valley price differences to achieve peak shaving and valley filling, thus reducing electricity costs.

[0057] The control flowchart is as follows: Based on the information of dispatchable DG, electric vehicles and loads, the distribution network optimization dispatch sends the peak shaving demand to each feeder (gate). The feeder then allocates the peak shaving indicators according to the adjustable capacity of each autonomous region to achieve the goal of reducing peak electricity consumption. During the off-peak hours, the reverse process of the peak dispatch is used.

[0058] The integrated energy collaborative management and control system provided by this invention has the following technical effects:

[0059] By fully leveraging the complementary characteristics of various energy sources, a collaborative management and control architecture for diverse heterogeneous energy sources was established. This approach explored energy efficiency improvement resources across all stages, including source-grid-load-storage, thereby enhancing the absorption capacity of renewable energy. It also achieved mutual complementarity and support among various energy sources, joint dispatch, and the digitization and visualization of energy consumption data across the entire planned area. This improved the efficient utilization of comprehensive energy and reliable power supply, thus resolving the technical problem of poor resource efficiency caused by the inability of existing network architectures to manage various energy sources.

[0060] Furthermore, regarding autonomous operation, the normal operation mode of the distribution network should be coordinated and integrated with the operation mode of the upstream power grid. Combined with the distribution automation system (DAS) control mode, feeder automation (FA) should be rationally utilized to give the distribution network a certain degree of self-healing capability. It should meet the power supply reliability and power quality requirements of customers of different importance levels, avoid situations where dual-power-supply customers are supplied with only one power source due to mode adjustments, and possess the ability to coordinate and assist between upstream and downstream power grids. Simultaneously, based on the location, load density, and operation and management needs of the upstream substations, it should be divided into several relatively independent zoned distribution networks. The power supply range of each zoned distribution network should be clearly defined, avoiding overlap and intersection, and adjacent zones should have appropriate interconnection channels. In addition, the distribution network operation mode should be adjusted in a timely manner to ensure a basic balance in the load distribution of all relevant interconnection lines and to meet the safe current-carrying capacity requirements of the lines.

[0061] In terms of energy operation and maintenance, intelligent alarms, centralized operation and maintenance, analysis and evaluation are achieved by collecting and monitoring the operation data of various energy stations such as distributed photovoltaics (the collected data includes telemetry, remote signaling, remote pulse, remote adjustment, event alarms, and energy consumption and operation data).

[0062] In terms of comprehensive application evaluation, based on the operational characteristics of smart energy, the adjustability potential, adjustability, reactive power support, and traceability of smart energy in supporting grid operation are assessed. Triggered by the operational characteristics of smart energy, the external characteristics of the region are evaluated, the adjustability potential is assessed, and the overall adjustability of the region is evaluated from aspects such as adjustment methods, control timing, and response methods.

[0063] Based on the above calculations, the external characteristics of regional energy are evaluated, including typical power generation curves, typical power consumption curves, fluctuation characteristics, regulation time characteristics, energy efficiency, and price response patterns. A simulation parameter library is also established to support the analysis of the entire network.

[0064] Equipment condition prediction requires the support of equipment condition monitoring and fault diagnosis technologies. Based on the characteristics of condition prediction, this study combines multiple algorithms, such as feature parameter prediction, curve fitting, time series model prediction, grey system prediction, support vector machine regression prediction, and neural network prediction, to seek the intrinsic relationship between equipment condition and various related factors. It also fully considers factors such as the operator's maintenance plan, thereby enabling a scientific prediction of future equipment health status.

[0065] The fundamental approach to equipment condition prediction is to study the patterns of equipment operational degradation based on historical data, in order to estimate and judge the equipment's operating condition over a certain period of time in the future. It can also provide warnings about abnormal operating conditions of equipment, assist in scientific equipment maintenance strategies, and thus influence the optimal scheduling of the entire system.

[0066] Based on users' energy consumption and supply data, load and energy day-ahead forecasts are made, the microgrid's flexible control margin is analyzed, and energy optimization strategies are formulated with the goals of minimizing operating costs and carbon emissions. Energy dispatch instructions are obtained from each terminal system, instructions are sent to user-side terminal systems, and the operating status of relevant equipment is monitored in real time to achieve local energy optimization.

[0067] The operation and control strategy is implemented through the cooperation of the energy management system, regional coordination controller, and local automatic control system. The control at the millisecond level is achieved by the coordination controller.

[0068] Regarding grid connection and off-grid switching, the off-grid switching conditions are prepared as follows: Based on the state of charge of the energy storage battery, one PCS is selected as the main power source. After receiving the signal, the main PCS returns a preparatory signal to the host. Power adjustment: The main PCS is adjusted to have zero output power; then the output of the slave PCS is adjusted so that the grid connection point's power is less than a set value (within the main PCS's power range). Instantaneously, it is determined that the sum of the grid connection point's power and the main PCS's power is less than the set value. If this is not met, the condition is met by load shedding or increasing power generation. Off-grid switching: The main PCS is controlled to switch from PQ mode to VSG mode (command execution time approximately 30ms); after a certain delay, the grid connection switch is disconnected.

[0069] For planned grid connection, the grid connection interface device and the energy storage PCS, which serves as the main power supply, cooperate to achieve synchronous grid connection. The coordination control device sends a synchronization check and closing command to the grid connection interface device, and simultaneously sends a synchronization adjustment command to the main PCS, which actively adjusts the voltage and frequency. When the synchronous grid connection conditions are met, the grid connection interface device closes the grid connection switch; after a certain delay, the control energy storage switches from VSG mode to PQ mode.

[0070] Based on various energy production processes and procedures, we calculate and analyze the energy conversion efficiency, energy loss, and daily, weekly, monthly, and yearly energy consumption of various equipment. We analyze the energy utilization efficiency at each stage and seek methods to improve energy utilization efficiency. This includes energy efficiency analysis of key equipment, customer energy efficiency analysis, industry energy efficiency analysis, and energy efficiency curves.

[0071] The demonstration area will eventually include multiple industrial park-type microgrids, residential microgrids, and smart building-type microgrids, forming a microgrid cluster. These different microgrids have varying energy supply and consumption characteristics, presenting potential for multi-energy complementarity in time and space. Furthermore, since the various microgrids and distributed energy suppliers within the regional smart energy system belong to different operating entities, a centralized dispatch framework is not suitable. A distributed dispatch strategy is required, with each operating entity cooperating and making autonomous decisions within a limited information exchange environment.

[0072] In the event of an accident, the system can respond to the frequency and voltage regulation needs of the main power grid within seconds by utilizing adjustable resources within the emergency control area. Based on its own operational status, it can adjust adjustable resources, utilizing energy storage within the area as a power source to transmit electricity to the upper-level grid. Simultaneously, it calculates the actual energy contribution of the system and estimates the adjustable margin and the duration of emergency support.

[0073] Virtual power plants can respond to invitations based on pre-issued instructions from the upper-level power grid. These pre-issued instructions include specific indicators (demand response capacity, price, revenue, system operating trends, etc.) and a timeframe for the invitation. Within the specified timeframe, by adjusting adjustable resources (flexible loads, energy storage systems, river water source systems, etc.), the agreed-upon operating indicators and relevant real-time operating trends are obtained.

[0074] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, model prediction, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0075] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A comprehensive energy collaborative management and control system, characterized in that, The system includes: The optimized scheduling layer includes a cloud platform, an energy management system, a digital twin system, a user-side virtual power plant, and an external interaction system. The coordination and control layer includes a central coordination and control system, regional coordination controllers, grid connection interface devices, and a thermal coordination and control system. The regional coordination controllers are communicatively connected to the energy management system. The regional coordination controllers include a multi-level functional application layer, specifically including at least one of a high-level application layer, a basic application layer, a platform support layer, and a platform service layer. The high-level application layer includes at least one of an operation control module, a comprehensive evaluation module, an optimized scheduling module, a microgrid control module, a grid support module, and a transaction auxiliary decision-making module. The basic application layer includes at least one of a data service module, a comprehensive monitoring module, a multi-energy prediction module, and an operation indicator monitoring module. The sensing and control layer is communicatively connected to the regional coordination controller and sends the data acquired by the sensing and control layer to the regional coordination controller so that the regional coordination controller can formulate and output a cooperative control strategy based on the data acquired by the sensing and control layer.

2. The integrated energy collaborative management and control system according to claim 1, characterized in that, The sensing and control layer includes a microgrid fault recorder, an energy controller, a local control system, a power distribution IoT terminal, meters, sensors, and a gateway. The microgrid fault recorder, energy controller, local control system, distribution IoT terminal, meter, sensor and gateway will transmit the collected data to the regional coordination controller.

3. The integrated energy collaborative management and control system according to claim 1, characterized in that, The data service module is used to receive massive amounts of data acquired by the perception and control layer, and to clean the massive amounts of data to obtain an ordered dataset.

4. The integrated energy collaborative management and control system according to claim 1, characterized in that, The data service module is also used to intercept at least one of the broadcast message, point-to-point message reception status, and point-to-point message transmission status during the data transmission process, and to monitor the data transmission process based on the intercepted data.

5. The integrated energy collaborative management and control system according to claim 1, characterized in that, The data service module is also used to acquire network traffic and system resource traffic, and to monitor the data transmission process based on the network traffic and system resource traffic.

6. The integrated energy collaborative management and control system according to any one of claims 3 to 5, characterized in that, The integrated monitoring module is used to output the monitoring results and to output alarm information when abnormal monitoring results occur.

7. The integrated energy collaborative management and control system according to claim 1, characterized in that, The multi-energy prediction module is used to predict the power generation of each energy system and output the prediction results.

8. The integrated energy collaborative management and control system according to claim 1, characterized in that, The operation control module is used to jointly schedule controllable resources, which include electrical energy, cooling, heating and gas resources.

9. The integrated energy collaborative management and control system according to claim 1, characterized in that, The comprehensive evaluation module is used to evaluate the overall adjustability of the region through adjustment methods, control time, and response methods.

10. The integrated energy collaborative management and control system according to claim 1, characterized in that, The optimization scheduling module is used to obtain optimization instructions and control strategies, and issue instructions to the user-side terminal system based on the control strategies.

11. The integrated energy collaborative management and control system according to claim 10, characterized in that, The control strategy is issued collaboratively by the energy management system, the regional coordination controller, and the local automatic control system.

12. The integrated energy collaborative management and control system according to claim 1, characterized in that, The power grid support module is used to issue response commands when an accident is confirmed.

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