Sustainable and Efficient Renewable-Driven Energy Trading via Neural-Enhanced Time-Adaptive Robust Nash Bargaining between Hydrogen-Enriched Gas and Active Distribution Networks
For multi-energy network operators, this work addresses privacy and uncertainty challenges in cross-sector coordination, though the improvement is incremental over existing ADMM-based methods.
The paper proposes a neural-enhanced time-adaptive robust Nash bargaining strategy for energy trading between hydrogen-enriched gas and active distribution networks, achieving stable social welfare (1.6% of total cost) and reducing runtime by 69.86% to 102.47s without economic loss.
Integrated hydrogen-enriched compressed natural gas (HCNG) and active distribution network (ADN) is providing efficient and sustainable flexibility for consuming renewable energies. Yet, cross-sector privacy and uncertain high-renewable scenarios block stable coordination. They also worsen decision performance and convergence. To conquer the barrier, a neural enhanced time-adaptive robust Nash bargaining strategy is proposed.In the first stage, to clear energy trading between ADN and gas distribution network (GDN) and promote its sustainability, a privacy preserved Nash Bargaining based on the alternating direction method of multipliers (ADMM) is applied. The next robust dispatch stage explores the worst renewable scenarios and derisks ADNs profit collapse from uncertainties. The convergence of the entire energy trading scheme is theoretically proved. As such, sustainable returns from the participation of solid oxide fuel cell (SOFC) and HCNG are facilitated. Finally, a time complexity and social welfare co-driven neural-enhanced time-adaptive strategy is proposed. The strategy assesses the influence of time resolution on social benefits and solving time in multi-energy trading. Based on the assessment, a neural network surrogate model is trained to accelerate the trading process in a close looped manner. Numerical assessment reveals that, the proposed strategy reaps a stable social welfare of nearly 1.6% to total cost, and benefit-steady situations for both ADN and GDN, even in the worst renewable scenarios. Moreover, it reduces runtime to 102.47s, improving computational efficiency by over 69.86% versus the fixed time-scale baseline, almost without sacrifice in economy.