Developed an Optimization Model Using Pyomo to Optimize Water Energy Nexus in Agriculture

This research focuses on enhancing an individualized water-energy nexus model with particular reference to agricultural use. A solar integrated system is used to drive water pumps for irrigation and household demand. The key aim here is optimizing operational costs associated with power consumption, battery applications, pumping water, and system switching actions. This research achieves a significant reduction in the overall operation cost by using advanced optimization techniques with GNU Linear Programming Kit of a Python-based optimization library (Pyomo). It is an open-source library. A mathematical framework is introduced to improve operational management, incorporating both solar power generation and water requirements to meet agricultural needs. This study also examines the complex switching between inverter sources and battery charging modes in energy storage, emphasizing the need for a balanced approach in managing the water-energy nexus. By improving system switching and storage operations, it advances strategies for sustainable water and energy use. Using Pyomo's flexibility to tackle complex optimization, the study addresses unique challenges in sustainable energy and water systems. The model provides a practical, cost-effective framework for resource management in agriculture, offering valuable insights into sustainable energy solutions.