Cyclic storage approach for conjunctive operation of surface and ground water systems (Case study: ZarrinehRoud catchment area)

Abstract

Abstract In this research, the problem of optimal conjunctive operation of surface and ground water system is investigated considering cyclic storage approach. This problem is solved here using mathematical programming and some efficient meta heuristic algorithms. For this purpose, the mathematical model of this system is defined and firstly solved by nonlinear programming (NLP) method. In addition, the performance of artificial bee colony (ABC) algorithm, genetic algorithm (GA), gravitational search algorithm (GSA) and particle swarm optimization (PSO) algorithms are also studied to solve this problem. Here, two case studies, means a hypothetical benchmark system and conjunctive use of Buchan dam reservoir and Miandoab aquifer located in the catchment area of ​​Urmia Lake (ZarrinehRoud catchment area) as a real problem, are considered to study the performance of proposed methods. For the hypothetical benchmark system, the results show that the optimal operating cost and related computational time are equal to 5.2428 Billion Rials and 5400 second, respectively, obtained using the NLP method. In addition, the operation costs are increased 26.36%, 26.1%, 44.91% and 21.28% in comparison with result of NLP method using ABC, GA, GSA and PSO algorithms, respectively. However, the computational time is extremely decreased in comparison with the related value of NLP method using these algorithms. For the real benchmark system, the results show that the optimal operating cost and related computational time are equal to 139.0145 Billion Rials and 259200 second, respectively, obtained using the NLP method. In addition, the operation costs are increased 43.74%, 32.32%, and 50.57% in comparison with result of NLP method using ABC, GA and PSO algorithms, respectively. However, the computational time is extremely decreased in comparison with related value of NLP method using this algorithm. Furthermore, the water demands of these problems are fully stratified using proposed methods. The obtained results show the efficiency and affectivity of the used method to solve more this complex optimization problem.