Quantum Learning and Estimation for Distribution Networks and Energy Communities Coordination

2025-06-13

AI Summary

Coordinated scheduling of distribution networks (DNs) and energy communities (ECs) faces challenges of limited information availability—only aggregated load data is accessible—and high computational overhead in modeling high-dimensional uncertainties. Method: This paper proposes an end-to-end quantum-enhanced learning and estimation framework. It introduces the novel Q-TCN-LSTM hybrid model to accurately map price signals to community-level responses, and integrates quantum amplitude estimation (QAE) with a dual-phase rotation circuit to accelerate risk-aware optimization. Contribution/Results: Experiments show that Q-TCN-LSTM achieves 69.2% higher mapping accuracy than classical models, reduces parameter count by 99.75%, and cuts inference time by 93.9%. QAE accelerates uncertainty quantification by 99.99% over Monte Carlo simulation, drastically lowering sampling requirements. The framework establishes a scalable, low-overhead quantum-intelligent paradigm for distributed energy coordination under information constraints and high-dimensional uncertainty.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Price signals from distribution networks (DNs) guide energy communities (ECs) to adjust energy usage, enabling effective coordination for reliable power system operation. However, this coordination faces significant challenges due to the limited availability of information (i.e., only the aggregated energy usage of ECs is available to DNs), and the high computational burden of accounting for uncertainties and the associated risks through numerous scenarios. To address these challenges, we propose a quantum learning and estimation approach to enhance coordination between DNs and ECs. Specifically, leveraging advanced quantum properties such as quantum superposition and entanglement, we develop a hybrid quantum temporal convolutional network-long short-term memory (Q-TCN-LSTM) model to establish an end-to-end mapping between ECs' responses and the price incentives from DNs. Moreover, we develop a quantum estimation method based on quantum amplitude estimation (QAE) and two phase-rotation circuits to significantly accelerate the optimization process under numerous uncertainty scenarios. Numerical experiments demonstrate that, compared to classical neural networks, the proposed Q-TCN-LSTM model improves the mapping accuracy by 69.2% while reducing the model size by 99.75% and the computation time by 93.9%. Compared to classical Monte Carlo simulation, QAE achieves comparable accuracy with a dramatic reduction in computational time (up to 99.99%) and requires significantly fewer computational resources.

Problem

• Enhance DN-EC coordination with limited information availability
• Reduce computational burden of uncertainty and risk scenarios
• Improve accuracy and efficiency using quantum learning techniques

Innovation

• Hybrid quantum TCN-LSTM model for mapping
• Quantum amplitude estimation accelerates optimization
• Quantum properties reduce computation time significantly