Energy Internet load optimization schemes integrate advanced algorithms, hierarchical control, and multi-objective planning to enhance efficiency, reliability, and renewable energy utilization.

Overview

Energy Internet (EI) represents a modern energy system that combines renewable energy, distributed energy resources (DERs), and advanced information technology to create a reliable, efficient, and adaptive energy network. Load optimization within EI focuses on balancing energy supply and demand, integrating intermittent renewable sources, and managing flexible loads such as electric vehicles (EVs) while maintaining network stability and operational efficiency .

Key Optimization Approaches

1. Hierarchical and Layered Control Models Hierarchical optimization divides the EI into layers, allowing distributed devices to plan electricity, heat, gas, and cooling efficiently. Layered control–collaborative optimization enables regional systems to coordinate multi-energy scheduling, improving independence of distributed equipment and minimizing operational costs . 2. Multi-Objective Optimization Algorithms Algorithms like self-adaptive NSGA-III (SA-NSGA-III) optimize EI network topology for connectivity, robustness, and operational efficiency. These methods preserve scale-free network characteristics while dynamically adjusting reference points and population selection, achieving significant improvements in network performance and convergence efficiency . 3. Deep Learning and Reinforcement Learning Integration Advanced schemes leverage graph neural networks (GNNs) and spatiotemporal feature fusion to capture network dynamics. Coupled with Soft Actor-Critic reinforcement learning, these frameworks optimize access location, capacity, and timing decisions under uncertain renewable generation and load fluctuations, enhancing reliability and economic performance . 4. Integrated Planning and Co-Optimization Comprehensive EI planning involves co-optimizing generation, transmission, distribution, and DERs. Iterative feedback loops and least-cost optimization frameworks allow planners to evaluate DER contributions, flexible load performance, and bulk-grid interactions, ensuring cost-effective and feasible solutions .

Benefits

• Enhanced Renewable Integration: Optimizes intermittent sources like solar and wind while mitigating voltage instability.
• Load Flexibility Management: Efficiently schedules EV charging and other dynamic loads to reduce peak demand.
• Operational Efficiency: Reduces energy costs and improves network robustness against failures or attacks.
• Scalability: Supports large-scale EI networks with multiple energy forms and distributed devices.

Practical Implementation

Successful EI load optimization requires granular data on resources, loads, and grid constraints, along with adaptive algorithms capable of real-time decision-making. Combining hierarchical control, multi-objective optimization, and AI-driven predictive models ensures that EI systems can meet growing energy demands while supporting carbon neutrality and sustainable energy goals .