Entropy-Based Information--Energy Optimization for Integrated Energy Systems With High RES Penetration

The operational optimization of integrated energy systems with high renewable energy penetration (IES-HREP) constitutes a complex systems engineering problem, primarily due to the absence of a well-defined cost model for renewable energy sources (RESs) generation and uncertainties affecting energy quality. To address these issues, this article proposes an entropy-based analysis and optimization framework to quantify RES uncertainty costs and system efficiency. First, an equivalent fuel (EF) cost model is introduced, integrating energy and information layers to quantify the cost of mitigating RES uncertainty. Building on this, entropy theory is employed to establish an information–energy quality coefficient (I-EQC) that evaluates RES energy quality by unifying thermodynamic and information entropy. In addition, a neurodynamics-based distributed algorithm is developed to perform multiobjective optimization for cost and exergy efficiency, enhancing computational speed while preserving data privacy. The simulation results demonstrate that the proposed framework reduces the cost by up to about 10% and improves the efficiency by up to about 5% compared to existing methods.