Demand-Response Energy Management for Renewable-Based Electric Ship Microgrids
DOI:
https://doi.org/10.31272/jeasd.4021Keywords:
Artificial neural network, Demand response, Electric ship microgrid, Renewable energy systems, Surrogate ModelAbstract
Electrically driven ships operate as self-contained renewable microgrids that combine power from solar and wind, along with battery energy storage, to supply propulsion and auxiliary loads. Their operation depends on the real-time coordination of distributed sources under varying environmental and load conditions. This study introduces an Artificial Neural Network-based surrogate model for the Demand-Response Energy Management System (DREMS) framework that performs real-time scheduling of renewable and dispatchable sources to meet the load requirement. This lightweight model serves as a data-driven surrogate for estimating solar and wind power outputs using meteorological data as inputs. These estimates are used by the DREMS, together with load and storage conditions, to determine optimal scheduling decisions for all power units. The approach is validated through simulations in MATLAB/Simulink using practical wind and irradiance profiles from publicly available data. The simulation results demonstrate the capability of the proposed framework to maintain supply-demand balance, coordinate renewable-energy unit commitment, and support stable operation under varying renewable-generation and load conditions. Although the study focuses on an electric-driven ship, the proposed framework can be applied to isolated renewable microgrids with similar operating conditions.
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Copyright (c) 2026 Naveen Kumar Thotakanama, NAGARAJA RAO SULAKE, Manickavasagam Krishnan (Author)
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