Distributed energy encompasses a wide range of technologies, including wind power, photovoltaics, energy storage systems, and combined heat, power, and cooling (CHP) systems. It also includes integrated storage solutions such as hydrogen fuel cells and lithium-based batteries. With the trend toward smaller, more fragmented loads, these systems are increasingly being integrated into the energy network, offering significant advantages. These benefits include improved utilization of low-carbon or carbon-free energy sources, promoting a greener energy ecosystem, and enhancing the flexibility of renewable energy integration. However, the intermittent and volatile nature of large-scale renewable energy access can impact grid stability and power trading, posing challenges to the safety and reliability of the power system. Additionally, without precise control, distributed energy systems may become uncontrollable, leading to inefficiencies and potential risks to the energy network. Therefore, microgrids have emerged as an effective solution, enabling intelligent real-time management and control of distributed energy on the user side, supporting high penetration, efficiency, and compatibility in energy applications. As demand for green energy grows, microgrid applications will play a key role in facilitating multi-energy complementary systems.
With the increasing integration of low-carbon energy and evolving energy internet technologies, distributed energy systems are expected to become a dominant mode of operation in the future. For Chinese energy companies traditionally focused on fossil fuels and centralized new energy projects, the development of distributed energy represents a crucial strategic direction. Actively developing smart microgrid cloud platforms based on distributed energy is not only vital for the green transformation of large energy enterprises but also contributes significantly to the global energy revolution.
Research and Application Status of Intelligent Microgrid Cloud Platform Technology
The intelligent microgrid for distributed energy aims to enable flexible and efficient utilization of decentralized resources at the medium and low voltage distribution level, addressing challenges related to the seamless integration and grid-connected operation of diverse distributed energy sources. It requires advanced, real-time, and intelligent energy management functions that reduce operational complexity and enhance renewable energy integration. This necessitates a smart microgrid cloud platform capable of handling massive device connectivity and high-speed big data processing. With growing research in both theory and practice, and rapid advancements in cloud computing, microgrid cloud platform technology is evolving quickly.
Various cloud platform initiatives have been proposed in the domestic energy sector, such as the State Grid Corporation's SG186 and ERP systems, and China Southern Power Grid’s SOA-based enterprise information systems. In the IT industry, companies like Google, Microsoft, and IBM have developed cloud computing platforms, including Amazon EC2, Google App Engine, and Sun Grid. Academic research has also explored the potential of cloud computing in energy systems.
Current research on cloud computing in smart microgrids focuses on three main areas: 1) Integration and management of heterogeneous data from multiple sources, 2) Distributed storage and management of large-scale energy data using technologies like BigTable, and 3) Parallel computation for fast microgrid analysis. Several demonstration projects worldwide have implemented cloud-based management platforms, where tasks are assigned to different resource nodes. The dynamic and heterogeneous infrastructure of cloud platforms presents challenges for task scheduling, requiring efficient algorithms to optimize performance and throughput.
In recent years, heuristic algorithms such as genetic algorithms and simulated annealing have gained attention for their effectiveness in task scheduling. Researchers like Buyya have introduced models like DBC (Deadline and Budget Constrained) scheduling, which consider both time and cost optimization. Beyond scheduling, the design of smart microgrid cloud platforms must address multi-source energy integration, resource virtualization, and cloud service architecture selection. Efficient task scheduling and self-healing capabilities remain critical research areas.
Key Characteristics and Technologies of the Intelligent Microgrid Cloud Platform
Smart microgrids for distributed energy offer several key features: support for diverse renewable generation, fast isolation response, grid connection and islanding capability, energy storage for load leveling, high reliability, intelligent energy management, multi-level microgrid support, and compatibility with existing power systems. The intelligent microgrid cloud platform is designed to leverage cloud computing, enabling developers to create SaaS applications efficiently. It serves system developers rather than end users directly.
The platform’s functional characteristics include wide-format compatibility, intelligent monitoring, real-time analysis, accurate prediction, and agile control. To realize these, key technologies such as multi-source data acquisition, advanced sensing, multidimensional indexing, distribution automation, and ETL processes are essential. These technologies ensure reliable data collection, efficient processing, and accurate decision-making.
System Design of the Intelligent Microgrid Cloud Platform Architecture
The cloud platform is structured into four layers: data acquisition, transmission, processing/storage, and display. It uses a Hadoop-based big data solution to handle large volumes of data. The data architecture supports both relational and non-relational databases, ensuring scalability and security. The platform demonstrates strong technical performance, including real-time data processing, fast business interface handling, ultra-fast big data exchange, and high reliability.
Application Prospects of the Intelligent Microgrid Cloud Platform
Developing a smart microgrid cloud platform offers vast application potential, especially in optimizing energy use across commercial buildings, schools, hospitals, and science parks. By identifying anomalies and improving scheduling, it enables more efficient energy allocation and consumption. The platform’s efficiency, low cost, intelligence, and reproducibility make it a valuable tool for future distributed energy management, suitable for widespread implementation.
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