Irrigation Load Optimization for Enhanced Agricultural Productivity in Rural Microgrid Clusters

Raj Vignesh Karunakaran, Rohitaa Ravikumar, Karthni Lakshmanan, Marjerie Suresh, Vineeth Vijayaraghavan

Overview

• Developed an algorithm to improve irrigation load in rural microgrid clusters using Python.
• Enhances the irrigation load efficiency by 9.1 percent and amount of irrigation by 22.6 percent.
• Published in the 2020 Annual IEEE Global Humanitarian Technology Conference proceedings. (here)

Citation

R. V. Karunakaran, R. Ravikumar, K. Lakshmanan, M. Suresh and V. Vijayaraghavan, “Irrigation Load Optimization for Enhanced Agricultural Productivity in Rural Microgrid Clusters,” 2020 IEEE Global Humanitarian Technology Conference (GHTC), Seattle, WA, USA, 2020, pp. 1-7, doi: 10.1109/GHTC46280.2020.9342922.

Abstract

This paper proposes a robust architecture for clustering pre-existing rural Indian standalone microgrids in close proximity to enhance the irrigation efficiency of the system leading to improved agricultural productivity along with alleviated overall costs. The Intelligent Energy Dispatching Grid (IEDG) comprises of Clustered Grid System (CGS) where the individual microgrids are integrated with each other in a participatory framework for effective energy dispatching and Centralized Storage Agent (CSA) which is annexed with the existing islanded system for effective storage and utilization of excess energy. Preferential servicing for agricultural loads is adopted to elevate the irrigation load efficiency under the Irrigation Load Preference (ILP) model. The proposed framework is implemented for a cluster of three incorporated microgrids where an increase of 22.61% is observed in the amount of water pumped resulting in a 9.1% increase in irrigation load servicing while preserving the domestic efficiency and lifetime cost of the system.