Simulation–optimization model for optimum water allocation between environmental and agricultural demand using a coupled WEAP-MODFLOW model: Application in Miyandoab plain, Urmia basin, Iran

2020 ◽  
Author(s):  
Amirhossein Dehghanipour ◽  
Gerrit Schoups ◽  
Bagher Zahabiyoun
Keyword(s):  
Optimization Model ◽  
Water Allocation ◽  
Deficit Irrigation ◽  
Simulation Optimization ◽  
Environmental Flow ◽  
Objective Functions ◽  
Trade Off ◽  
Agricultural Demand ◽  
Hydrologic Module ◽  
Optimum Water

<p>In this study, we developed a simulation-optimization model for optimum water allocation to meet environmental flow requirements and agricultural demand. The simulation model consists of three modules: a hydrologic module, an agronomic module, and an economic module. The hydrologic module is based on a dynamic coupling of WEAP and MODFLOW, and includes water balances for the crop root zone, the surface water system, and the underlying aquifer. The agronomic module simulates the effect of deficit irrigation on crop yield response in each growth stage, while the economic module calculates the net benefit of crop production. The optimization model contains two objective functions, one related to agricultural production and the other related to environmental flows. These conflicting objective functions are maximized using the Multi-Objective Particle Swarm Optimization algorithm. Decision variables include crop acreages, minimum environmental flow requirements in the river, and the degree of deficit irrigation. We applied the simulation-optimization model to the irrigated Miyandoab plain in the semi-arid northwest of Iran, for the historical period 1984 to 2013. There is competition between irrigation demands in the plain and environmental flow requirements to downstream Lake Urmia, which has been shrinking in recent years due to decreased inflows. Our results quantify what the (Pareto) trade-off looks like between meeting environmental and agricultural water demand in the region. We find that historical water allocations were suboptimal and that both agricultural and environmental benefits can be increased by better management of cropping decisions, deficit irrigation, and environmental flow requirements. We further show that increased groundwater use for irrigation can partly alleviate the trade-off, but that it leads to significant declines in groundwater levels due to the relatively small specific yield of the aquifer.</p>

Adaptive multi-objective simulation–optimization framework for dynamic flood control operation in a river–reservoir system

2015 ◽  
Vol 46 (6) ◽  
pp. 893-911 ◽  
Author(s):  
Om Prakash ◽  
K. Srinivasan ◽  
K. P. Sudheer
Keyword(s):  
Optimization Model ◽  
Flood Control ◽  
Simulation Optimization ◽  
Control Point ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Control Operation ◽  
Multi Objective ◽  
River Reservoir ◽  
Flood Control Operation

An adaptive simulation–optimization (S–O) framework enables dynamic reservoir operational decision-making process during the different phases (time stages) of flood control operation during the passage of a flood event in a river–reservoir system is proposed. This is achieved by incorporating the changing priorities of the reservoir operator/manager at each phase of the flood mitigation operation into the S–O framework by evoking the appropriate set of objective functions and dynamically reconstructing the multi-objective optimization model. Five different objective functions are formulated within the S–O framework, out of which two are concerned with the mitigation at the reservoir; two more deal with the mitigation at the control point; and one ensures sufficient water is stored for meeting future demands. The non-dominated sorting genetic algorithm-II (NSGA-II) is employed to obtain the trade-off solutions from the multi-objective optimization model at each time stage. The results from the study show that the dynamic flood operation model yields a significant level of improvement in flood peak mitigation over the static model both at the reservoir as well as at the control point. The proposed S–O framework can be used in developing either deterministic or probabilistic optimal reservoir release policies for flood control operation, especially where damage functions and penalty functions are not developed.

scholarly journals A Multiobjective Scheduling Optimization Model for Multienergy Complementary System Integrated by Wind-Photovoltaic-Convention Gas Turbines considering Demand Response

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Zhang Lihui ◽  
Xin He ◽  
Ju Liwei
Keyword(s):  
Demand Response ◽  
Optimization Model ◽  
Gas Turbines ◽  
Risk Level ◽  
Objective Functions ◽  
Entropy Weight ◽  
Scheduling Optimization ◽  
Power Sources ◽  
Complementary System ◽  
Scheduling Model

To utilize the complementary feature of different power sources, wind power plant (WPP), and solar photovoltaic power (PV), convention gas turbines (CGT) and incentive-based demand response (IBDR) are integrated into a multienergy complementary system (MECS) with the implementation of price-based demand response (PBDR). Firstly, the power output model of WPP, PV, and CGT is constructed and the mathematical model of DR is presented. Then, a multiobjective scheduling model is proposed for MECS operation under the objective functions of the maximum economic benefit, the minimum abandoned energy, and the minimum risk level. Thirdly, the payoff table of objective functions is put forward for converting the multiobjective model into a single objective model by using entropy weight method to calculate weighting coefficients of different objective functions. Finally, the improved IEEE 30 bus system is taken as the simulation system with four simulation scenarios for comparatively analyzing the influence of PBDR and IBDR on MECS operation. The simulation results show the following: (1) The MECS fully utilized the complementarity of different power sources; CGT and IBDR can provide peaking service for WPP and PV to optimize overall system operation. (2) The proposed algorithm can solve the MECS multiobjective scheduling optimization model, and the system scheduling results in the comprehensive optimal mode can take into account different appeal. And the total revenue, abandoned energy capacity, and load fluctuation are, respectively, 108009.30¥, 11.62 MW h, and 9.74 MW. (3) PBDR and IBDR have significant synergistic optimization effects, which can promote the grid connection of WPP and PV. When they are both introduced, the peak-to-valley ratio of the load curve is 1.19, and the abandoned energy is 5.85 MW h. Therefore, the proposed MECS scheduling model and solution algorithm could provide the decision basis for decision makers based on their actual situation.

Water allocation under deficit irrigation using MIKE BASIN model for the mitigation of climate change

2015 ◽  
Vol 33 (6) ◽  
pp. 469-482 ◽  
Author(s):  
Charalampos Doulgeris ◽  
Pantazis Georgiou ◽  
Dimitris Papadimos ◽  
Dimitris Papamichail
Keyword(s):  
Climate Change ◽  
Water Allocation ◽  
Deficit Irrigation
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