A comprehensive energy optimization and coordination system

Abstract

This invention relates to a comprehensive energy optimization and coordination system, comprising a cloud server located in the cloud and a park station server located in a park. The park is equipped with new energy power generation equipment, new energy power consumption equipment, and energy storage equipment, with the energy storage equipment connected to the new energy power generation equipment and power consumption equipment within the same park. The park station server collects data from the new energy power generation equipment, power consumption equipment, and energy storage equipment connected to the same park, collecting parameters of each discharge process of the new energy power consumption equipment, power generation process parameters, and charging and discharging parameters of the energy storage equipment, and uploads the collected data to the cloud server as historical data. The park station server also controls the new energy power generation equipment connected to the same park. Based on the historical data, the cloud server predicts the electricity load of the corresponding park and sends the prediction results to the corresponding park server. The park server controls the power generation time and power output of the corresponding new energy power generation equipment according to the prediction results.

Description

Technical Field

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

[0002] The energy internet uses integrated energy systems as its physical carrier and deeply integrates intelligent information technology to achieve interconnection, mutual assistance, and plug-and-play functionality of different energy sources, creating a "quasi-internet" energy system.

[0003] Distributed power sources and microgrid systems can coordinate and control local power sources, energy storage devices, and loads, improving the spatiotemporal imbalance of different energy sources under varying supply and demand conditions. This aims to reduce system energy costs, improve energy efficiency, and enhance power supply reliability. Simultaneously, it helps smooth peak-valley or abnormal fluctuations in the main power grid, thereby improving its stability and reliability. However, distributed energy sources such as wind and solar power are greatly affected by environmental factors and cannot achieve stable and continuous power supply on their own. The existence of multiple energy flows, including electricity, heat / cooling, leads to various calculation standards and methods, making direct comparison and evaluation impossible. Furthermore, the increasing complexity of equipment types and structures continuously increases the data scale and solution difficulty of related problems.

[0004] In practical applications, different users have different actual needs, requiring the construction of different architectural systems to address these needs, which brings difficulties to the preparation and forecasting of electricity consumption. Furthermore, when users have new functional requirements, it is also necessary to consider adding new energy service platform systems, making it even more difficult to rationally arrange and schedule the power generation utilizing new energy sources. Summary of the Invention

[0005] The purpose of this invention is to provide a comprehensive energy optimization and coordination system to solve the problem of the difficulty in rationally arranging and scheduling new energy power generation during the coordination and control process.

[0006] To achieve the above objectives, the present invention includes:

[0007] This invention discloses a comprehensive energy optimization and coordination system, comprising a cloud server located in the cloud and a park station server located in a park. The park is equipped with new energy power generation equipment, new energy power consumption equipment, and energy storage equipment. The energy storage equipment is connected to the new energy power generation equipment and new energy power consumption equipment within the same park. The park station server collects data from the new energy power generation equipment, new energy power consumption equipment, and energy storage equipment connected to the same park, collecting parameters of each discharge process of the new energy power consumption equipment, parameters of the power generation process of the new energy power generation equipment, and parameters of the charge and discharge of the energy storage equipment. The collected data is then uploaded to the cloud server as historical data. The park station server also controls the new energy power generation equipment connected to the same park. The cloud server predicts the electricity load of the corresponding park based on the historical data and sends the prediction results to the corresponding park server. The park server controls the power generation time and power output of the corresponding new energy power generation equipment according to the prediction results.

[0008] The beneficial effects of this approach are as follows: This invention predicts the electricity consumption of a community or commercial park by statistically analyzing data on renewable energy generation, consumption, and energy storage. Based on these predictions, it then controls the renewable energy generation equipment. For example, during the peak nighttime charging period for electric vehicles, if the peak electricity consumption and corresponding load are predicted to be from 8 PM to 7 AM the following morning, the renewable energy generation equipment can be controlled to generate electricity evenly during the period of highest renewable energy generation efficiency before the peak and store it in energy storage devices. This invention enables planned control of renewable energy generation, preventing renewable energy generation equipment from operating at full power and load for extended periods, or from working during periods of low renewable energy efficiency, thereby improving renewable energy generation efficiency, reducing the power load, and extending the lifespan of renewable energy generation equipment.

[0009] Furthermore, new energy power generation equipment includes photovoltaic power plants, wind power plants, geothermal power plants, and substations that import clean energy.

[0010] The benefits of this approach are as follows: By combining photovoltaic power plants, wind power plants, geothermal power plants, and imported clean energy sources, we can achieve horizontal energy complementarity and fully explore and utilize the direct complementarity and substitutability of different energy sources. This not only enables the output of heat, electricity, and cooling, but also facilitates energy exchange between light and electricity, heat and cooling, wind and electricity, and direct and alternating current, thereby achieving optimal energy utilization efficiency.

[0011] Furthermore, electrical equipment includes electric vehicle charging stations.

[0012] Furthermore, cloud server systems are configured with a network layer, a data layer, and a business layer.

[0013] Furthermore, the network layer uses the SuperSocket framework.

[0014] Furthermore, Redis message queues are set up between each pair of the network layer, data layer, and business layer.

[0015] Furthermore, the data layer includes databases, which consist of a business database and a historical database.

[0016] The benefits of doing this are as follows: by setting up business databases and historical databases in the database, and by connecting the data layer with the business layer, the response speed of the data layer to the business layer is improved, which in turn improves the system's response speed to business.

[0017] Furthermore, the business database uses one of the following databases: NoSQL database, Redis database, or MongoDB database.

[0018] Furthermore, the historical database uses a MySQL database.

[0019] Furthermore, the business layer includes a device monitoring module that performs real-time monitoring of the corresponding devices based on the historical data, and an energy management module for performing charging and discharging data statistics, discharging prediction, and charging control. Attached Figure Description

[0020] Figure 1 This is a diagram of the integrated energy multi-level system architecture of the present invention;

[0021] Figure 2 This is a schematic diagram of the front-end architecture of the integrated energy management system of the present invention;

[0022] Figure 3 This is a schematic diagram of the business services of the integrated energy management system of the present invention. Detailed Implementation

[0023] The present invention will now be described in further detail with reference to the accompanying drawings.

[0024] Example:

[0025] Integrated energy systems are designed and proposed in the context of the energy production and consumption revolution and the "Internet+" era, with electricity at their core. They are complex systems that integrate information and energy based on Internet thinking and concepts. Their purpose is to promote energy structure adjustment and significantly improve the efficiency of energy in transmission, distribution and end-use. They are hybrid multi-energy systems composed of integrated cooling, heating and power systems, natural gas pipelines and energy storage units, etc., which are the physical core of integrated energy systems and embody the various links of integrated energy "source-grid-load-storage".

[0026] This invention begins with the research of unified modeling theory and methods for multi-energy complementary networks. Through fully digital simulation of multi-energy complementary networks involving electricity, gas, and heat, it achieves the functions of security defense and optimized scheduling of multi-energy complementary networks based on simulation. In practical applications, it achieves horizontal complementarity of multiple energy sources, fully exploring and utilizing the direct complementarity and substitution of different energy sources. It not only enables the output of heat, electricity, and cooling, but also facilitates energy exchange between light / electricity, heat / cooling, wind / electricity, and DC / AC, achieving optimal energy utilization efficiency. Furthermore, it constructs a multi-energy flow semi-physical digital simulation platform, achieving simulation effects difficult to achieve with digital models. This allows for the verification and correction of digital models using physical models, providing a debugging and verification platform for the development of controllers and protection devices. Finally, it develops an integrated energy management system and key supporting devices, and conducts practical verification and improvement in a specific industrial park.

[0027] This invention provides a comprehensive energy optimization and coordination control system, such as... Figure 1 The diagram shows a multi-level integrated energy system architecture, including a central station layer, regional station layers, and park station layers. The central station layer contains a central master station system (visual monitoring center), which communicates with third-party systems, including computers of government agencies, computers of operators, mobile devices of maintenance and repair personnel, and mobile devices of users, factories, communities, and other customers. The regional station layer contains regional virtual systems (cloud) and regional physical systems (cloud). The park station layer contains several park station subsystems and park station data acquisition concentrators, and the various park station subsystems can communicate with each other.

[0028] like Figure 2 The diagram shows the architecture of the front-end machine (a physical system that mainly acts as a gateway) of the integrated energy management system of the present invention, including the network layer, data layer, and service layer.

[0029] The network layer uses the SuperSocket framework to integrate the received information initialized according to the configuration, the received information, and the information that the data enters the message queue into the Redis message queue.

[0030] The SuperSocket framework is a lightweight, cross-platform, and scalable .NET / Mono Socket server framework. It features high-performance event-driven communication, powerful and high-performance protocol parsing tools, multi-listener support, application domain and process-level isolation, and session-level send queues that allow for concurrent data sending within sessions while maintaining high performance and controllability. The core idea is to create applications around business domain components. These applications can be developed, managed, and accelerated independently. Using a microservices cloud architecture and platform across these distributed components simplifies deployment, management, and service delivery. Microservices thoroughly componentize and service-orient business systems. A single business system is broken down into multiple smaller applications that can be independently developed, designed, run, and maintained. These smaller applications interact and integrate through services. Each smaller application, from the front end to the control layer, logic layer, and database access (including the database itself), is completely independent and can be deployed as needed (identical services can be repeatedly deployed to meet processing requirements).

[0031] Redis is a remote dictionary service, which is essentially a database.

[0032] Redis message queues send information to the data layer. The data layer has a protocol parsing module and a database. The protocol parsing module parses the messages in the Redis message queue and generates real-time business data and alarm data. The databases in the data layer are completely independent and can be deployed as needed (the same service can be repeatedly deployed to meet processing requirements). The system databases in the data layer are divided into business databases and historical databases. The business database can be a NoSQL database, a Redis database, or a MongoDB database, while the historical database uses MySQL. The time complexity of lookups and operations in the Redis database is 0 or 1, resulting in fast concurrency and response speed. It supports rich data types, and all operations are atomic. Atomicity means that changes to data are either fully executed or not executed at all. Upper-layer business processes are completed through the business database, improving the system's response speed. The historical database can be configured as a cluster, dual-machine, or single-machine database depending on the data volume.

[0033] Real-time data and alarm data from the data layer are integrated and input into the Redis message queue of the business layer. The Redis message queue and the database together provide data support for the basic services of the business layer, including real-time data services, alarm data services, configuration data services, and historical data services. In addition to basic services, other business services are also provided. The basic services and other business services are integrated and input into the WebAPI, which is the application programming interface of the World Wide Web.

[0034] like Figure 3 The diagram shown is a schematic of the business services of the integrated energy management system of the present invention, which includes three parts: equipment monitoring, energy management, and user management.

[0035] Equipment monitoring includes power consumption monitoring, energy storage monitoring, and photovoltaic (PV) monitoring. Power consumption monitoring includes real-time monitoring of the primary system diagram, 48-hour power generation and consumption distribution curves, and real-time grid monitoring. Energy storage monitoring refers to energy storage charging and discharging monitoring. PV monitoring includes real-time PV monitoring and PV data statistical monitoring. Among these, power consumption monitoring is used to measure and collect power indicators in real time to ensure the observation, recording, and dynamic analysis of the basic operating conditions of the power system. This provides a complete understanding of the technical and economic conditions for the safe, stable, and high-quality operation of the power grid, and allows for a comprehensive evaluation of various power indicators to optimize the entire system's monitoring structure and facilitate data sharing and exchange.

[0036] Energy management encompasses charging management and energy consumption analysis. Charging management includes vehicle charging and discharging monitoring, historical charging and discharging data for vehicles and energy storage, and historical charging and discharging data for energy storage. Energy consumption analysis includes time-of-use load power analysis, time-of-use electricity consumption statistical analysis, energy consumption classification and itemized statistics, energy consumption trend analysis, and report services / time-of-use and object-specific queries. Energy consumption analysis, using time as a dimension, statistically analyzes the power curves of different electricity consumption types, including base load, adjustable load, photovoltaic, and energy storage. It records power and electricity consumption data for different time periods. Through the accumulation of a large amount of raw data, it predicts power load, analyzes the trends and rationality of user electricity load demand, summarizes load characteristic patterns, rationally arranges predicted load, ensures power reliability and safety, and provides support for the rational scheduling, important power supply protection work, and grid load adjustment of the overall power grid. Charging management refers to the system's ability to completely record all parameters of each charging and discharging process of vehicles and energy storage batteries, supporting multi-dimensional queries, statistics, export, and management.

[0037] User management includes access control and login / registration. Access control includes user permission settings, operation logs, control logs, and system logs. User permissions are set and changed under the authorization of higher-level users. Operation logs record system operations, such as page switching, system data queries, and device operations. Control logs record information about system controls, including time, user, and which controls were performed, serving as an important basis for subsequent control system analysis.

[0038] The specific method for constructing dynamic models of power equipment (source, grid, load, and storage) according to this invention is as follows:

[0039] 1) Power sources and energy storage are built using the Simulink library and a self-built dynamic model. The Simulink library connects a series of modules to form a complex system model.

[0040] 2) Based on the definition of energy conservation as controllable electrical load, determine the conversion and thermal / cold storage equipment;

[0041] 3) Construct a load with adjustable RLC parameters.

[0042] The grid coordination control algorithm under multi-source access in this invention specifically includes: a peer-to-peer control mode, constructing a droop control model for each distributed power source, realizing decoupled control of the active and reactive power of the inverter, allowing some systems to continue to maintain power supply in the form of wind power or photovoltaic islands, in which case master-slave control is adopted, and the grid coordination control simulation system under multi-source access is developed based on the Simulink environment. The grid coordination control under multi-source access achieves the line voltage fluctuation range within 7% as required by national standards, and the grid frequency fluctuation range within 0.2-0.5Hz as required by national standards.

[0043] The specific method for constructing a comprehensive energy optimization and coordination control system according to this invention is as follows:

[0044] 1) Simulation modeling of integrated energy multi-energy complementary networks:

[0045] a. Design of an interconnection and interoperability solution based on multiple energy sources and information:

[0046] By utilizing renewable energy sources such as geothermal, photovoltaic, and waste heat from sewage in specific regions, external electricity can be converted into renewable and clean energy.

[0047] To fully guarantee the energy needs of the new energy internet town area, a clean, zero-carbon, diversified energy supply model should be constructed to form a synergistic and complementary smart energy system. The plan includes the introduction of clean electricity from external sources, combined with regional planning schemes, to build a clean, safe, and reliable energy security system, providing users with comprehensive, flexible, and convenient services, and realizing intelligent interaction between people and the energy system. Simultaneously, a series of valuable references and suggestions on integrated energy applications should be submitted.

[0048] b. Modeling of multi-energy complementary networks including electricity, gas, and cooling / heating:

[0049] This research focuses on information modeling technology for integrated energy systems with power grids at their core and multiple complementary energy forms. Energy production and transmission in multi-energy complementary networks involve dynamic processes across multiple time scales, including thermodynamics, pneumatics, electrodynamics, and mechanical transmission. These processes involve the mutual conversion of thermal energy, electrical energy, mechanical energy, chemical energy, and other forms of energy. The thermodynamic characteristics of these systems significantly affect their operational energy efficiency, while their dynamic characteristics determine the overall safety level of the system.

[0050] c. Research on model correction and trend prediction techniques:

[0051] Deep learning models are built on neural networks, which consist of many neurons, each a processing unit. Each neuron first applies a non-linear activation function to process the input data and then outputs the result. It is through this process that neural networks are able to represent non-linear relationships. Recurrent neural networks (RNNs) are general models that utilize neural networks to process sequences. They are capable of handling correlations between inputs, but their drawback is their inability to correlate information over long time spans. Therefore, LSTM (Long Short Term) networks are introduced to address this problem. LSTM is a special type of RNN that can learn long-term dependencies. Proposed by Hochreiter and Schmidhuber in 1997, LSTM deliberately avoids the long-term dependency problem, allowing it to remember long-term information in practice. Therefore, using LSTM models to predict regular power consumption over any time span (day, month, or year) is feasible.

[0052] Trend forecasting requires analysis over a time dimension, predicting trends based on past energy changes. However, existing trend forecasting methods suffer from various drawbacks, including declining accuracy in long-term forecasts, insufficient handling of patterns, and limited applicability. Deep learning models effectively extract useful information from complex data to study dynamically changing sequences and uncover hidden patterns within seemingly disordered data. Furthermore, the LSTM network in deep learning possesses long-term memory capabilities, making it more suitable for energy trend forecasting across various time and geographical spans. With increasing industrial and economic development and rising demands for energy conservation and emission reduction, using deep learning models for trend forecasting is an inevitable trend.

[0053] For simulation models, real-time measurement data can be combined with deep learning LSTM networks. By analyzing the trends in input and output over a period of time, the underlying patterns hidden in seemingly disordered data can be identified. This can be compared with the corresponding simulation model through a black-box approach. By adjusting the factor variables, the accuracy of the digital simulation model can be continuously improved.

[0054] 2) Multi-functional complementary network security defense, planning, design, and optimized scheduling:

[0055] a. Rapid security defense strategy based on cascading failure simulation:

[0056] Multi-energy complementary network devices are highly diverse and random, and faults may propagate through multiple coupled systems. It is necessary to study the evolution mechanism and propagation path of cascading faults, and then design rapid security defense strategies.

[0057] b. Planning and design based on simulation of the medium- and long-term evolution of multi-energy complementary networks:

[0058] The medium- and long-term evolution process of multi-energy complementary networks is studied based on a simulation platform. A system planning and design method is proposed and applied to practical multi-energy complementary networks.

[0059] c. Optimized scheduling strategy based on dynamic simulation analysis and trend prediction:

[0060] Research and adopt dynamic simulation analysis methods to promptly detect abnormal fluctuations in the system and power grid, and achieve rapid automatic adjustment through automatic simulation processing.

[0061] 3) Integrated Energy Semi-Physical Simulation Platform:

[0062] Hardware-in-the-loop (HIL) simulation is a simulation technique that incorporates physical components into a simulation system, creating a simulation loop with the participation of physical hardware. Its advantage is that it allows components whose models cannot be accurately created to directly enter the simulation loop. By switching between the model and the physical component, the model can be further verified, and the impact of the physical model component on system performance can be confirmed. Essentially, it creates a simulation environment that mimics the actual physical components, using physical components for simulation.

[0063] Building a semi-physical digital simulation platform can achieve simulation effects that are difficult to achieve with digital models, enable the verification and correction of digital models by physical models, and provide a debugging and verification platform for the development of controllers and protection devices.

[0064] a. Building a simulation hardware platform:

[0065] The system utilizes a simulation host, a host machine running the digital model, and I / O interface machines, configured with numerous AI, AO, DI, and DO converters. These are connected to the simulation target machine (physical model machine) via analog and digital input / output. High-speed data acquisition and response are crucial for ensuring the simulation's real-time performance. The system also includes the target machine, simulation control console, and other auxiliary equipment. The simulation target machine can be a semi-physical model of primary equipment, or a controller or protection device. The simulation control console is used to monitor and control the real-time simulation operation status. Other auxiliary equipment includes instruments such as displays, recorders, and analyzers.

[0066] b. Target simulation research based on a hardware-in-the-loop simulation system:

[0067] This research focuses on simulation techniques for electrical targets such as sources, loads, and storage devices based on hardware-in-the-loop (HIL) simulation systems. By combining I / O systems and power drive modules, simulations for various application scenarios are achieved.

[0068] This research focuses on simulation technology for cold and heat sensing and control targets based on hardware-in-the-loop (HIL) simulation systems. Combining I / O systems, the system employs a design that integrates cooling and heating devices with sensors and controllers to simulate various application scenarios.

[0069] 4) Multi-energy complementary network simulation and energy management integrated platform

[0070] a. Integrated platform software development

[0071] Develop and implement an integrated energy management system. Combined with a simulation computing engine, it achieves general functions applicable to integrated energy management, such as integrated numerical simulation, data visualization, real-time centralized monitoring, benchmarking analysis, and decision support. It establishes basic information such as master data and metadata related to integrated energy, realizes real-time dynamic simulation analysis of integrated energy, implements a trend prediction subsystem, and realizes an intelligent scheduling subsystem based on dynamic load forecasting.

[0072] b. Practical application and improvement of integrated platforms in specific industrial parks

[0073] A specific industrial park was selected as an application demonstration base. Based on the park's actual energy consumption, a multi-energy complementary network simulation and energy management integrated platform system was deployed. Through actual system operation, verification, modification, and improvement, the system was put into practical use.

[0074] This invention utilizes fully digital simulation of multi-energy complementary networks (electricity, gas, heat, etc.) to achieve security defense and optimized scheduling functions based on simulation. In practical applications, it achieves horizontal complementarity of multiple energy sources, fully exploring and utilizing the direct complementarity and substitution of different energy sources. This not only enables the output of heat, electricity, and cooling but also facilitates energy exchange between light / electricity, heat / cooling, wind / electricity, and DC / AC, achieving optimal energy utilization efficiency. Furthermore, it constructs a multi-energy flow semi-physical digital simulation platform, achieving simulation effects difficult to achieve with digital models. This allows for the verification and correction of digital models using physical models, providing a debugging and verification platform for the development of controllers and protection devices. Finally, a comprehensive energy management system and key supporting devices were developed and put into practical application and improvement in a specific park. By utilizing geothermal, photovoltaic, and waste heat from sewage in a specific region, and using external electricity as renewable and clean energy, the energy demand of the new energy internet town area is fully guaranteed. It is suitable to build a clean, zero-carbon diversified energy supply model, form a synergistic and complementary smart energy system, plan for external clean electricity, and combine it with regional planning schemes to build a clean, safe, and reliable energy security system, providing users with comprehensive, flexible, and convenient services, and realizing intelligent interaction between people and the energy system.

[0075] Meanwhile, the multi-energy complementary network devices of this invention are diverse and highly random, and faults may propagate through multiple coupled systems. By studying and adopting dynamic simulation analysis, abnormal fluctuations in the system and power grid can be detected in a timely manner. Through automatic simulation processing, rapid automatic adjustment can be achieved. Furthermore, by adding physical components to the simulation system, a simulation technology with physical hardware involved in the loop can be formed. Components that cannot be accurately modeled can be directly entered into the simulation loop. By switching between the model and the physical components, the model can be further verified, and the impact of physical model components on system performance can be verified. In essence, it creates a simulation environment that simulates reality for physical components. The simulation technology using physical components can build a semi-physical digital simulation platform, which can achieve simulation effects that are difficult to achieve with digital models. It can realize the verification and correction of digital models by physical models and provide a debugging and verification platform for the development of controllers and protection devices.

Claims

1. A comprehensive energy optimization and coordination system, characterized in that, The system includes a cloud server located in the cloud and a park station server located in the park. The park is equipped with new energy power generation equipment, new energy power consumption equipment, and energy storage equipment. The energy storage equipment is connected to the new energy power generation equipment and new energy power consumption equipment in the same park. The park station server collects data from the new energy power generation equipment, new energy power consumption equipment, and energy storage equipment connected to the same park. This data is used to collect parameters of each discharge process of the new energy power consumption equipment, the power generation process of the new energy power generation equipment, and the charging and discharging parameters of the energy storage equipment, and uploads the collected data to the cloud server as historical data. The park station server also controls the new energy power generation equipment connected to the same park. The cloud server predicts the electricity load of the corresponding park based on historical data and sends the prediction results to the corresponding park server. The park server controls the power generation time and power output of the corresponding new energy power generation equipment according to the prediction results. The system constructs dynamic models of power equipment in the source, grid, load, and storage systems, develops a grid coordination control simulation system under multi-source access, performs simulation modeling of integrated energy multi-energy complementary networks, and realizes the functions of security defense, design, and optimized scheduling of multi-energy complementary networks based on simulation. The simulation modeling of the integrated energy multi-energy complementary network includes: design of interconnection schemes based on multiple energy sources and information; modeling of electric, gas, and cooling / heating multi-energy complementary networks; prediction of energy change trends using real-time measurement data combined with a deep learning LSTM network to correct the simulation model; and the security defense, design, and optimized scheduling of the multi-energy complementary network include: a rapid security defense strategy based on cascading failure simulation; planning and design based on simulation of the medium- and long-term evolution process of the multi-energy complementary network; and optimized scheduling strategies based on dynamic simulation analysis and trend prediction.

2. The integrated energy optimization and coordination system according to claim 1, characterized in that, A multi-energy flow semi-physical digital simulation platform is constructed to realize the verification and correction of the digital model by the physical model. The construction method is as follows: using a simulation host, a host machine for running the digital model, and an I / O interface machine, a large number of AI, AO, DI, and DO ports are configured, including the simulation target machine, the simulation control console, and other auxiliary devices, and connected to the simulation target machine through analog and digital input and output. Research on target simulation based on hardware-in-the-loop simulation system to realize simulation of various application scenarios.

3. The integrated energy optimization and coordination system according to claim 2, characterized in that, The grid coordination control simulation system under multi-source access involves grid coordination control algorithms including peer control mode, droop control model of each distributed power source, decoupling control of active power and reactive power of inverter and master-slave control.

4. The integrated energy optimization and coordination system according to claim 3, characterized in that, The cloud server system is configured with a network layer, a data layer, and a business layer.

5. The integrated energy optimization and coordination system according to claim 4, characterized in that, The network layer uses the SuperSocket framework.

6. The integrated energy optimization and coordination system according to claim 5, characterized in that, The network layer, data layer, and business layer are connected by Redis message queues.

7. The integrated energy optimization and coordination system according to claim 6, characterized in that, The data layer is equipped with a database, which includes a business database and a historical database.

8. The integrated energy optimization and coordination system according to claim 7, characterized in that, The business database uses one of the following databases: NoSQL database, Redis database, or MongoDB database.

9. The integrated energy optimization and coordination system according to claim 8, characterized in that, The historical database uses a MySQL database.

10. The integrated energy optimization and coordination system according to claim 9, characterized in that, The business layer includes a device monitoring module that performs real-time monitoring of the corresponding devices based on the historical data, and an energy management module for performing charging and discharging data statistics, discharging prediction, and charging control.

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