Techno-economic sizing of constrained microgrids using multivariate deep learning and Dc-coupled solar-plus-storage integration
TL;DR
The results demonstrate that intelligently maximizing zero-emission resources actively suppresses compounding carbon liabilities (averting a 21.6% financial premium), providing system planners with a resilient, data-driven methodology to optimize infrastructure investments without over-sizing centralized grid connections.
Abstract
The transition toward reliable and sustainable microgrids in constrained power systems requires both accurate load demand forecasting and the intelligent resource coordination. The primary objective of this study is to optimize the capacity sizing and operational resilience of such systems by proposing an Artificial Intelligence-driven Load Forecasting and Battery-Integrated Energy Management approach, structured around a DC-coupled solar-plus-storage (DC-CS-P-S) architecture. To accurately capture the complex thermodynamic and behavioural patterns driving electricity consumption, a multivariate long short-term memory (LSTM) network was developed. The proposed strategy physically decouples energy storage recovery from the grid by dedicating solar photovoltaic generation strictly to charging the battery energy storage system to ensure availability for peak shaving and essential load protection. Evaluated over an 8760-hour simulation, the LSTM forecasting achieved high predictive accuracy with a mean absolute percentage error of 3.31% and an RMSE of 12.39 kW. Under severe bottleneck constraints, the DC-coupled battery actively contributed 93.64 kW of peak shaving power, successfully diverting 100% of the energy deficit (135 020 kWh) entirely to non-essential infrastructure. Furthermore, a comprehensive techno-economic parametric analysis, incorporating a value of lost load reliability penalty, and a $50/ton carbon price, identified 330 kW as the absolute optimal grid capacity. At this threshold, the microgrid achieves complete reliability with zero energy shedding while maintaining a minimized baseline operational costs of $92 033 USD. Ultimately, the results demonstrate that intelligently maximizing zero-emission resources actively suppresses compounding carbon liabilities (averting a 21.6% financial premium), providing system planners with a resilient, data-driven methodology to optimize infrastructure investments without over-sizing centralized grid connections.