Optimization of Hybrid Renewable Energy Systems: Develop Advanced Control Algorithms and Modeling Techniques

EasyChair Preprint 13858

Abstract

The integration of renewable energy sources, such as solar and wind power, into modern energy grids presents both challenges and opportunities. Hybrid renewable energy systems (HRES), which combine multiple renewable sources and energy storage, offer a promising approach to improve the reliability, efficiency, and cost-effectiveness of renewable energy integration. However, the optimal design and control of HRES remains a complex problem due to the intermittent and stochastic nature of renewable resources.

This paper focuses on the development of advanced control algorithms and modeling techniques for the optimization of HRES. We propose a novel multi-objective optimization framework that considers the technical, economic, and environmental aspects of HRES design. The framework incorporates advanced control strategies, such as model predictive control and reinforcement learning, to dynamically manage the dispatch of renewable generation, energy storage, and conventional backup sources.

Additionally, we present new modeling approaches that capture the uncertainty and variability of renewable resources through the use of probabilistic forecasting and stochastic programming techniques. These models are integrated into the optimization framework to enable robust decision-making under uncertainty.

The proposed methods are evaluated through case studies using real-world data and simulations. The results demonstrate significant improvements in the performance and reliability of HRES compared to traditional control and optimization approaches. The developed techniques have the potential to accelerate the widespread adoption of hybrid renewable energy systems and contribute to the transition towards a sustainable and resilient energy future.

Keyphrases: Energy, Thermal, heat, power

@booklet{EasyChair:13858,
  author    = {Axel Egon and Lucas Doris},
  title     = {Optimization of Hybrid Renewable Energy Systems: Develop Advanced Control Algorithms and Modeling Techniques},
  howpublished = {EasyChair Preprint 13858},
  year      = {EasyChair, 2024}}