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. As the trend toward miniaturization and fragmentation of energy loads continues, distributed energy is increasingly being integrated into the broader energy network. This integration offers significant advantages, such as improving the utilization rate of low-carbon or carbon-free energy sources, promoting the greening of the energy ecosystem, and enabling more efficient use of renewable resources. However, the intermittent and variable nature of large-scale renewable energy integration can pose challenges to grid stability and power trading, potentially impacting the safety and reliability of the power system. Without precise control, a large number of distributed energy systems can become uncontrollable, leading to inefficiencies and even threats to grid security. Therefore, integrating distributed energy into microgrids allows for intelligent, real-time management and control, supporting high penetration, efficiency, and compatibility in distributed energy applications. As economic and social development drives demand for green energy, the importance of microgrid applications and multi-energy complementary systems becomes increasingly evident.
With the integration of low-carbon energy and the evolution of energy internet technologies, distributed energy systems are expected to become the dominant mode of energy operation in the future. For Chinese energy enterprises that traditionally focus on fossil fuels and centralized new energy projects, the development of distributed energy represents a crucial strategic direction that requires significant attention. Actively developing smart microgrid cloud platforms based on distributed energy is essential not only for the green transformation of large energy companies but also for contributing to the global energy revolution.
Research and Application Status of Intelligent Microgrid Cloud Platform Technology
An intelligent microgrid designed for distributed energy aims to enable flexible and efficient utilization at the medium and low voltage distribution level, addressing challenges related to the seamless access and grid-connected operation of diverse and large-scale distributed energy sources. These systems require real-time, high-intelligence energy management functions to reduce the complexity of system operators and enhance the integration of renewable energy. To achieve this, an intelligent microgrid cloud platform is necessary, featuring capabilities like interactive clouds and data clouds. These platforms must support a rapidly growing number of connected devices and provide high-speed, multi-threaded big data processing. With ongoing research in both theory and practice, and the rapid advancement of cloud computing technology, the development of microgrid cloud platforms is progressing quickly.
Various cloud platform initiatives have been proposed in the domestic energy industry, such as the State Grid Corporation’s SG186 and SG-ERP systems, and China Southern Power Grid’s SOA-based enterprise information systems. In the IT sector, companies like Google, Microsoft, and IBM have launched cloud computing platforms such as Amazon EC2, Google App Engine, Sun Grid, and Aneka. Academically, researchers are exploring the potential of cloud computing in energy applications.
Currently, the application of cloud computing in smart microgrids focuses on three main areas: 1) Integration and management of multi-source heterogeneous data; 2) Distributed storage and management of massive data; and 3) Parallel computation and analysis of microgrid systems. Several microgrid demonstration projects worldwide have implemented intelligent management platforms based on cloud computing. Task scheduling across different resource nodes is a critical challenge due to the dynamic and heterogeneous nature of cloud infrastructure. Efficient task allocation strategies are needed to optimize performance and ensure reliable execution.
In recent years, heuristic algorithms such as genetic algorithms and simulated annealing have gained traction in task scheduling research. Australia’s Buyya introduced the DBC (Deadline and Budget Constrained) algorithm, which considers both time and cost optimization. Beyond traditional scheduling methods, the integration of economic models into energy resource management has also become widespread.
Key features of intelligent microgrid platforms include support for various renewable energy sources, fast isolation response, grid connectivity, energy storage, high reliability, intelligent energy management, multi-level microgrid support, and adaptability to existing power systems. The cloud platform serves as a foundation for developers to create SaaS applications, enabling efficient and scalable solutions for smart microgrid management.
Functional characteristics of the intelligent microgrid cloud platform include wide-format compatibility, intelligent monitoring, real-time analysis, accurate prediction, and agile control. Key technologies required for its development include multi-source data acquisition, advanced sensing, multidimensional indexing, distribution automation, advanced metering architecture, and ETL technology.
System design of the intelligent microgrid cloud platform includes four layers: data acquisition, transmission, processing, and display. The architecture leverages big data solutions from the Hadoop ecosystem. The platform has demonstrated strong technical performance, including real-time data processing, high concurrency, ultra-fast big data exchange, and high reliability.
The application prospects of the intelligent microgrid cloud platform are vast, offering opportunities for optimizing energy use, reducing costs, and enhancing sustainability. As a powerful tool for distributed energy management, it is well-suited for widespread implementation and future growth.
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