Distributed energy includes not only wind power, photovoltaic, energy storage and cold/heat/electrical combined systems, but also integrated storage systems such as hydrogen fuel cells and lithium-powered batteries, with the trend of load miniaturization and fragmentation, in integration Apparent to the energy network, it has obvious advantages, namely, improving the utilization rate of low-carbon or carbon-free energy, promoting the greening of the energy ecology, and the intermittent and volatility of large-scale access will directly affect the grid operation and power trading. The impact of the impact on the safety and stability of the power system; on the other hand, a large number of distributed energy grids without the precise control, will cause the energy system to be uncontrollable, operating inefficiency, and even harm the energy network security, Therefore, in the form of micro-grid (Micro-Grid), distributed energy is absorbed on the user side, realizing intelligent real-time management and control, forming intelligent intelligence that contributes to high proportion, high efficiency and high compatibility of distributed energy applications. Microgrid applications, as economic and social developments continue to grow in demand for green energy, and facilitate the realization of complementary forms of multiple energy sources To pay attention to.
With the integration of low-carbon energy and supply and demand, coupled with the continuous evolution of energy Internet technology, distributed energy systems will gradually become the main energy operation mode in the future. For energy enterprises in China, which mainly focus on traditional fossil energy and centralized large-scale new energy business, the development of distributed energy is an important strategic direction that deserves high attention. Active development of smart microgrid cloud platform based on distributed energy should be actively developed. Related technologies and business solutions are of great significance to the green energy transformation and upgrading of large energy companies themselves and to the global energy revolution.
Research and Application Status of Intelligent Microgrid Cloud Platform Technology
The intelligent microgrid for distributed energy is designed to realize the flexible and efficient application of distributed energy at the medium and low voltage distribution system level, and to solve the main problems of large-scale and diverse distributed energy seamless access and grid-connected operation. At the same time, it is necessary to have real-time and high-intelligence energy management functions, which effectively reduces the scheduling difficulty of system operators and enhances the access capability of renewable energy. This requires a smart microgrid cloud platform system, including interactive clouds, data clouds, etc., capable of carrying the number of connected devices with geometric progression, and high-speed multi-threaded big data processing capabilities. With the continuous accumulation of research on the theory and practice of intelligent microgrid at home and abroad, based on the rapid development of cloud computing technology, the technical research on the microgrid cloud platform is also rapidly evolving.
Various cloud platform construction plans or similar plans have been proposed in the domestic energy industry, including the State Grid Corporation's SG186 and the State Grid Enterprise Resource Planning (SG-ERP), and China Southern Power Grid Corporation's SOA-based enterprise-level information system. In the IT industry, many large companies, including Goole, MicroSoft, and IBM, are engaged in cloud computing research and practice, and have launched a series of cloud computing platforms, including Amazon ElasticComputeCloud, GoogleAppEngine, SunGrid, and Aneka. In the academic world, some researchers have begun to explore the application prospects of cloud computing technology in energy.
The current research and application of cloud computing technology in smart microgrid is mainly reflected in the following three aspects.
1) Integration and management of multi-source heterogeneous data sources. In the microgrid system, there are multiple energy application systems and different application platforms such as heat/electricity/cold cogeneration, internal combustion engine power generation, gas turbine power generation, small hydropower, wind power, photovoltaic power generation and hydrogen fuel cells. Different applications and different software and hardware providers and developers lead to the dispersion of energy data resources and obvious heterogeneity, making it difficult to share. The cloud platform utilizes virtualization technology to abstract different resources such as servers, networks, and applications into service forms, cleanse differences, and provide external services.
2) Distributed storage and management of massive data. Using distributed storage methods, using BigTable and other technologies, cloud computing can store massive amounts of energy data in a smart microgrid with reliable storage, real-time analysis and processing, and efficient management.
3) Parallel calculation and analysis of fast microgrid systems. Utilize the high-performance parallel processing and running capabilities of the cloud computing platform to achieve efficient calculation and analysis of multi-energy complementary and supply-demand matching strategies in smart microgrids. There are several microgrid demonstration projects at home and abroad, and an intelligent management and control platform based on cloud computing technology is built. According to the information of the task, different tasks are assigned to different resource nodes to run according to the appropriate information. Because the infrastructure of the cloud computing platform is heterogeneous and dynamic, this puts a severe test on the task allocation strategy of the grid. The inefficient task allocation strategy will inevitably increase the execution time of the task and reduce the overall cloud computing. The throughput of the system. For the different types of task scheduling in the smart grid, how to efficiently allocate and utilize distributed resources requires some effective scheduling algorithms.
There are several microgrid demonstration projects at home and abroad, and an intelligent management and control platform based on cloud computing technology is built. According to the information of the task, different tasks are assigned to different resource nodes to run according to the appropriate information. Because the infrastructure of the cloud computing platform is heterogeneous and dynamic, this puts a severe test on the task allocation strategy of the grid. The inefficient task allocation strategy will inevitably increase the execution time of the task and reduce the overall cloud computing. The throughput of the system. For the different types of task scheduling in the smart grid, how to efficiently allocate and utilize distributed resources requires some effective scheduling algorithms.
In recent years, heuristic intelligent algorithms have become a major research direction of task scheduling problems. Classic heuristic algorithms mainly include Sufferage algorithm, Min-min algorithm, genetic algorithm (Genetic Algorithm, GA) and simulated annealing (Simu-latedAnnealing, SA) algorithm. At the same time, the use of economic models to portray the supply and demand of energy resources has gradually been widely used. Australia's Buyya proposed a DBC (Deadline and Budget Constrained) scheduling algorithm under the Grid Architecture definition of ComputingEconomy. Different from the traditional heuristic algorithm, in the algorithm, the calculation cost of the task is also taken into account as an important parameter, and it is divided into time optimization, cost optimization and conservative time optimization according to different focuses. The calculation time and calculation cost of the task are optimized as the main target.
In addition to the basic key issues of multi-source energy resource integration optimization, resource virtualization and cloud computing service architecture selection in cloud computing platforms, the specific requirements and characteristics of smart micro-grid can not be ignored, while the smart micro-grid cloud The efficient scheduling and self-healing of the platform is also the key to current application research.
The key to the intelligentization of the intelligent microgrid lies in the coordinated and efficient scheduling of multi-tasking of the system, and the requirements and reliance on real-time computing resources are increasing. In the smart microgrid cloud platform for distributed energy, the task environment to be scheduled by the cloud is for all kinds of distributed energy and related applications. Therefore, in order to adapt to the dynamic, real-time and security requirements in the smart micro-grid cloud platform environment, it is necessary to combine the existing research results, analyze the advantages and disadvantages of various scheduling algorithms, and study dynamic, distributed-oriented energy. Task scheduling algorithm. On the whole, the application research of cloud platform technology in smart microgrid is still in the exploration stage.
Characteristics and key technologies of intelligent microgrid platform for distributed energy
The main characteristics of smart microgrids for distributed energy can be summarized as:
1 Support a variety of new energy distributed generation;
2 fast isolation response, no impact on the large power grid;
3 can be connected to the grid or isolated network, plug and play, seamless switching;
4 has an energy storage system to support peak clipping and valley filling;
5 High reliability power supply, safe and stable operation;
6 with intelligent and efficient energy management functions to improve energy efficiency;
7 support multi-level microgrid;
8 Adapt to China's existing power management system.
The intelligent micro-grid cloud platform is a cloud platform designed and developed through the cloud computing technology, focusing on the characteristics of distributed energy applications and realizing the above eight main characteristics of the smart micro-grid. The so-called cloud platform refers to providing "cloud"-based services for developers of smart micro-grid management system to create applications; developers do not have to rebuild the development foundation, and can rely on the cloud platform to quickly and efficiently create a variety of new SaaS applications. . The direct users of the cloud platform are system developers of smart microgrids, not microgrid end users.
The functional characteristics of the intelligent microgrid cloud platform are mainly reflected in the following aspects:
1) Wide format compatible. Access to distributed energy, adaptive processing technology.
2) Intelligent monitoring. Adopt advanced intelligent measurement and sensing technology.
3) Real-time analysis. Efficiently improve data to information and optimize operation.
4) Accurate prediction. Reasonable prediction and distribution of power through model simulation and power flow analysis.
5) Agile control. Effective control of monitoring status.
To realize the functional characteristics of the above cloud platform, it is required to prepare key technologies in the early stage of development of the smart microgrid. The main key technologies are divided into private cloud services: providing computing, data storage, etc.; SaaS cloud services such as video analytics, machine learning, data analysis, artificial intelligence and blockchain; TOB custom cloud services; platform tools.
(1) Multi-source data acquisition and cleaning technology
Through a large number of sensors and smart meters, the production and consumption data of various distributed energy sources in the microgrid system are periodically fixed at a fixed frequency to form a cover of all nodes in the microgrid (control center, substation, segment switch and user port, etc.) The IP communication network uses optical networking, wireless and carrier networking technologies to support various distribution terminals and systems "on the Internet." The technology combines cloud-based data cleaning technology to achieve errors, omissions, and format differences in multi-source data collection, and to regulate, replenish, and clean.
(2) Advanced sensing measurement technology
Optical or electronic transformers, overhead line and cable temperature measurement, power equipment status online monitoring and power quality measurement technology, only data sensing and measurement, no data reliability identification. The advanced sensing measurement that combines the capabilities of cloud computing is based on data comparison, data testing, real-time analysis to determine the accuracy and reliability of data measurement, and to ensure the accuracy of the basic data of intelligent management of the micro-grid.
(3) Multidimensional indexing technology based on grid file
In the real-time data accurate call and analysis of the micro-grid system protection, off-grid efficient switching, production and demand matching optimization and carbon footprint tracking, the Hive data warehouse based on Google's Hadoop platform is commonly used. system. However, Hive's support for indexes is weak, and it is difficult to achieve multi-latitude data indexing. A distributed multidimensional index DGFIndex (DistributedGridFileIndex) based on Grid-File is being designed to improve multi-dimensional interval query performance.
(4) Advanced distribution automation technology
Current distribution automation technologies include power distribution automation (security monitoring and data acquisition, integrated automation and feeder automation), distribution management automation (distribution geographic information systems, equipment management and maintenance management, etc.) and demand side response automation. Aspects of the content. On this basis, the micro-grid cloud platform implements Advanced Distribution Automation (ADA), supports DER's “plug and playâ€, adopts IP technology, and emphasizes the standardization of system interfaces, data models and communication services. With openness.
(5) Advanced Metering Architecture (AMA)
Advanced measurement technology is a technical system that uses smart energy meters to communicate with ETL technology, use cloud computing capabilities, measure, collect and analyze user-side energy usage and consumption data on demand or in a set manner. The AMA is a key technology that supports user interaction and enables fast, real-time response on the demand side.
(6) ETL (Extract-Transform-Load) technology
ETL is an important part of building a data warehouse. Users extract the required data from the data source, clean it through data, and finally load the data into the data warehouse according to the predefined data warehouse model. This is a core technology built on cloud platforms based on various distributed energy sources, including Oracle, SQLServer, Sybase, DB2, and MySQL to achieve multi-source data consistency and integration with micro-grid multi-energy business scenarios.
System Design of Intelligent Microgrid Cloud Platform Architecture
(1) Cloud platform system rack
According to the flow of data, the design system is divided into four layers:
1 data acquisition layer: mainly through sensors (smart meter) to obtain energy consumption of each circuit and its related energy parameters, photovoltaic power generation, energy storage, hydrogen production and hydrogen consumption and other energy information;
2 data transmission layer: mainly converts energy data into TCP/IP protocol format and uploads it to the database of energy-saving management monitoring system;
3 data processing storage layer and display: the data storage layer is mainly responsible for the aggregation, statistics, analysis, processing and storage of energy consumption data;
4 data display layer: mainly display and release energy consumption data in the storage layer.
The design will use a big data storage and parallel computing system solution based on the Hadoop ecosystem.
Figure 1 Architecture diagram of intelligent microgrid cloud platform system for distributed energy
(2) Cloud platform data architecture design
The overall database selection of the cloud platform design is comprehensive consideration from the perspectives of openness, security and performance. At the same time, in addition to the relational database, in addition to the relational database, the non-relational database is strongly supplemented in combination with the requirements for processing, analysis and prediction of big data in this project. Improve the integrity and security of data storage and improve the efficiency of data processing and analysis.
In addition to considering the performance of the database, the design of the data architecture is more concerned with the integration of the data architecture and various distributed energy sources of Shenhua Science and Technology Park. The cloud platform is built to support multi-source data and multi-energy complementary data (structured data). Storage management and fast and efficient analysis of semi-structured data and unstructured data, enabling a second-order response to data queries at data scales from 100 TB to PB; capable of structured, semi-structured and unstructured Data is processed uniformly; a second-level response that enables full-text retrieval of 100 billion-level text entries. The platform data business architecture design is shown in Figure 2.
Figure 2 platform data business architecture system function diagram
The cloud platform architecture design has been experimentally tested and has the following technical performance advantages:
1) Real-time incremental data processing: Objects of more than 10 million levels are incremented by 600,000 at a time, and processing is completed within 20 minutes.
2) Key business interface processing: increase in 8 hours or more to 25 minutes, increase in 4 minutes or more to 15 minutes, and improve processing efficiency in 1 minute or more to 10 minutes.
3) Ultra-high-speed big data exchange: In the 10-node big data platform, the data exchange of 40 million records does not exceed 10 minutes, and the 30 million records do not exceed 6 minutes.
4) High-reliability big data platform: When the main control node fails, the HA node completes the drift takeover within 2s.
Application Prospect of Intelligent Microgrid Cloud Platform System
Develop a smart microgrid cloud platform for distributed energy, which is a big data platform with large-scale data, high concurrency, multi-process and self-developed core algorithms; using artificial intelligence, machine learning technology, combined with micro-grid characteristics, according to Applying the actual data of the scenario, building a cloud platform system that meets the requirements of distributed energy utilization and multi-energy complementary applications will have a broad application space.
The establishment and operation of the intelligent microgrid cloud platform system can effectively mine the energy consumption anomalies and energy consumption loopholes of various commercial buildings, schools, hospitals and science parks, and intelligently optimize the scheduling energy use scheme, and allocate and utilize each more rationally. Energy-like, thus more precise control of energy consumption. In order to provide a comfortable environment, help establish a mode of managing energy conservation, tap its own energy saving potential and combine technical energy saving measures to effectively reduce energy consumption and energy costs. The cloud platform system has the advantages of high efficiency, low cost, intelligence and reproducibility, and is an important tool for distributed energy management in the future, and is suitable for widespread application and implementation.
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