Optimal crop water allocation coupled with reservoir operation by Genetic Algorithm and Non-Linear Programming (GA-NLP) hybrid approach

Abstract

Abstract This paper presents a Genetic Algorithm and Non-Linear Programming (GA-NLP) hybrid model to derive steady state optimal reservoir operating policies for a reservoir. In the present study, the objective is maximizing the yields of all the crops in the command area considering yield response to water deficit subject to constraints on reservoir water balance, storage bounds, channel capacities and minimum water requirements. Decision variables of the model are fortnight water allocations to each crop grown under left & right main canals and d/s releases. The model developed is applied to the Nagarjunasagar reservoir in Andhra Pradesh, India. Various levels of dependable inflows entering into the reservoir (75%, 80% & 85%) are considered in the present study. Optimal policy obtained by proposed model are validated through simulation and compared with Standard operating policy (SOP). The results obtained using the proposed model gives guidelines to reservoir managers to take decisions. Results reveal that GA-NLP model can be effectively used for optimal allocation of limited available water resources to any reservoir.