Energy Internet optimization focuses on multi-energy coordination, cost reduction, and efficiency improvement through advanced scheduling and computational algorithms.

Regional Energy Internet Optimization

One notable case involves the regional Energy Internet, which integrates electricity, heat, and gas networks with distributed energy resources such as photovoltaic (PV) systems, wind turbines (WT), combined cooling, heating, and power (CCHP) units, and energy storage (ES) devices . A double-layer optimal scheduling model has been proposed, where the upper layer targets economic and environmental benefits, and the lower layer focuses on energy efficiency. Optimization considers factors like the heat-to-electric ratio of CCHP units, energy storage lifespan, and real-time electricity prices. Computational methods such as particle swarm optimization combined with individual variation ant colony optimization are used to achieve high accuracy and efficiency, resulting in energy savings, environmental protection, and economically optimal dispatching schemes .

Hierarchical and Grid Scheduling Optimization

Another case emphasizes hierarchical optimization and grid scheduling for multi-energy systems. This approach uses a layered control–collaborative optimization framework, allowing distributed devices to plan electricity, heat, cold, and gas in the most efficient way . The model employs real-number genetic algorithms to simulate optimal scheduling across regions, improving coordination between distributed equipment while minimizing costs. This method enhances energy and cost savings compared to conventional approaches and supports large-scale integration of renewable energy sources, such as PV systems, into the grid .

Key Features of Optimization Cases

• Multi-energy coordination: Electricity, heat, and gas flows are synchronized to maximize efficiency.
• Distributed energy integration: Renewable energy sources and storage devices are optimally scheduled to reduce reliance on centralized fossil fuels.
• Economic and environmental objectives: Optimization balances cost reduction with emission control and energy efficiency.
• Advanced computational algorithms: Techniques like particle swarm optimization, ant colony optimization, and genetic algorithms are applied to solve complex scheduling problems.
• Scalability: Models can be applied at regional, city, or national levels, adapting to different system architectures and load types .

Practical Implications

These optimization cases demonstrate that the Energy Internet can enhance energy efficiency, reduce operational costs, and support sustainable energy development. By integrating multi-energy systems with intelligent scheduling and advanced algorithms, operators can achieve real-time, controllable, and coordinated energy flows, which is essential for the transition to renewable and distributed energy systems .