scholarly journals Genetic algorithms optimization of hedging rules for operation of the multi-purpose Ubonratana Reservoir in Thailand

2014 ◽  
Vol 364 ◽  
pp. 507-512 ◽  
Author(s):  
C. Chiamsathit ◽  
A. J. Adeloye ◽  
B. Soudharajan
Keyword(s):  
Genetic Algorithm ◽  
Critical Level ◽  
Water Shortages ◽  
Reservoir Performance ◽  
Rule Curves ◽  
Decision Variables ◽  
Rule Curve ◽  
Hedging Policy ◽  
Hedging Rules ◽  
Northeastern Thailand

Abstract. This study has developed optimal hedging policies for the multi-purpose Ubonratana Reservoir in northeastern Thailand based on its existing rule curves. The hedging policy was applied whenever the reservoir storage falls below a critical level for each month of the year. The decision variables, i.e. the set of monthly storages defining the critical rule curve that triggers rationing and the rationing ratio, were optimized by genetic algorithm (GA). Both single stage (i.e. with one critical rule curve and one rationing ratio) and two-stage (with two critical rule curves and ratios) of the hedging policy were considered in the optimization. To test the effect of the optimized hedging policies on reservoir performance, simulations were carried out, forced alternatively with the existing rule curves (i.e. without hedging) and the two optimized hedging policies. Performance was summarized in terms of reliability (time- and volume-based) and vulnerability. The results showed that the vulnerability was significantly reduced by using the optimized hedging rules. However, the number of water shortages increased with the optimized rules, causing the time-based reliability to worsen significantly. This should not be of concern since, although the number of shortages increased, the associated shortage quantities on most of these additional occasions were small, leaving the volumetric reliability largely unchanged.

scholarly journals A Trade-Off Analysis of Adaptive and Non-Adaptive Future Optimized Rule Curves Based on Simulation Algorithm

2021 ◽  
Author(s):  
Mahnoosh Moghaddasi ◽  
Sedigheh Anvari ◽  
Najmeh Akhondi
Keyword(s):  
Climate Change ◽  
Water Shortage ◽  
Vulnerability Index ◽  
Adaptation To Climate Change ◽  
Reservoir Performance ◽  
Static Hedging ◽  
Rule Curve ◽  
Hedging Rules ◽  
Future Data ◽  
Adaptive Policies

Abstract This study aims to investigate the performance of Zarrineh Rud reservoir by implementing strategies for adaptation to climate change. Using sequent peak algorithm (SPA), the rule curve were simulated. Then, the optimal rule curve was procured through GA-SPA, aiming to minimize the water shortage. The future data were downscaled using SDSM based on CanEsm2 model and under RCP2.6 and RCP8.5. Finally, in view of environmental demand, reservoir performance indices were calculated for both non-adaptive and adaptive policies during all future periods (2020–2076). Results showed simulation with the static hedging rules managed to significantly reduce the average vulnerability index (by 60%) compared to no hedging, while the dynamic hedging rules outperformed static hedging rules only by 9%. Therefore, considering the insignificant improvement in reservoir performance using dynamic rules and their complexity, static hedging rules are recommended as the better option for adaptation during climate change.

scholarly journals Optimization of Reservoir Operation using Linear Programming

2020 ◽  
Vol 8 (5) ◽  
pp. 1028-1032
Keyword(s):  
Linear Programming ◽  
Reservoir Operation ◽  
Storage Capacity ◽  
Optimization Techniques ◽  
Water Levels ◽  
Rule Curves ◽  
Operating Policy ◽  
Decision Variables ◽  
Rule Curve ◽  
Operation Period

The paper aims to derive the optimal releases monthly through linear programming for a single purpose reservoir. The releases from the reservoir are usually based upon the rule curves or operating policy adopted. The rule curve is the storage, indicating the water levels to be maintained in-order to satisfy the demand during the operation period. Linear programming (LP) is one of the global optimization techniques that have gained popularity as a means to attain reservoir operation. In the present study Linear Programming was used to develop an operation policy for Hemavathy Reservoir, Hassan District Karnataka, India. The decision variables were monthly reservoir releases for irrigation and initial storages in reservoir at beginning of the month. The constraint bound for the reservoir releases was reservoir storage capacity. The results derived by using Linear Programming shows that the downstream irrigation demands were satisfied and also considerable amount of water was conserved from reduced spills.

scholarly journals Multi-UAV cooperative Route planning based on decision variables and improved genetic algorithm

2021 ◽  
Vol 1941 (1) ◽  
pp. 012012
Author(s):  
Jie Zhang ◽  
Ningzhou Li ◽  
Danyu Zhang ◽  
Xiaojuan Wei ◽  
Xiaojuan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Route Planning ◽  
Improved Genetic Algorithm ◽  
Decision Variables ◽  
Multi Uav

scholarly journals Optimizing management of the condensing heat and cooling of gases compression in oxy block using of a genetic algorithm

2013 ◽  
Vol 34 (4) ◽  
pp. 199-214
Author(s):  
Mateusz Brzęczek ◽  
Łukasz Bartela
Keyword(s):  
Genetic Algorithm ◽  
Power Plant ◽  
Flue Gas ◽  
Air Separation ◽  
Separation Unit ◽  
Supercritical Steam Parameters ◽  
Water Heaters ◽  
Decision Variables ◽  
Air Separation Unit ◽  
Steam Parameters

Abstract This paper presents the parameters of the reference oxy combustion block operating with supercritical steam parameters, equipped with an air separation unit and a carbon dioxide capture and compression installation. The possibility to recover the heat in the analyzed power plant is discussed. The decision variables and the thermodynamic functions for the optimization algorithm were identified. The principles of operation of genetic algorithm and methodology of conducted calculations are presented. The sensitivity analysis was performed for the best solutions to determine the effects of the selected variables on the power and efficiency of the unit. Optimization of the heat recovery from the air separation unit, flue gas condition and CO2 capture and compression installation using genetic algorithm was designed to replace the low-pressure section of the regenerative water heaters of steam cycle in analyzed unit. The result was to increase the power and efficiency of the entire power plant.

Optimization of Styrene Reactor Using Tabu Search and Genetic Algorithm Methods

2012 ◽  
Vol 9 (1) ◽  
Author(s):  
Shahrokh Shahhosseini ◽  
Samaneh Vakili
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Homogeneous Model ◽  
Ethyl Benzene ◽  
Catalytic Dehydrogenation ◽  
Algorithm Optimization ◽  
Decision Variables ◽  
Adiabatic Reactor ◽  
Very High ◽  
Styrene Conversion

This paper deals with multi-objective optimization of styrene reactor using Tabu search (TS) and genetic algorithm (GA) methods. Styrene is produced commercially by catalytic dehydrogenation of ethyl benzene. As styrene is an important monomer, the capacity of the plant is usually very high, as a result the investment cost is also very high, and even a small enhancement in the plant operation can generate major income. The adiabatic reactor using the pseudo homogeneous model was considered in this study for maximizing the styrene conversion and selectivity. A computer program was written to simulate an adiabatic reactor in order to evaluate the possibility of optimizing the process in simulation environment. The simulation results were compared with the experimental data. This comparison indicated that the value of overall mean squarer of errors for conversion of the compounds was 7.09E-05 and overall means relative error of them was 2.18 percent. In order to optimize the performance of the reactors, conversion of styrene was adopted as the objective function. Six decision variables, namely, ethyl benzene feed temperature at the entrance of each bed, pressure, the ratio of steam to ethyl benzene and initial ethyl benzene flow rate were used for the optimization. The results of GA optimization showed final conversion of styrene increased from an initial value of 0.710 to 0.75 after 100 generation of population. Applying Tabu algorithm optimization, the value rose from 0.725 to 0.813 after generation of 100 neighborhoods. The results revealed that the CPU time needed to optimize the reactor for TS was shorter than that of GA method. In addition, using the same iteration numbers for both methods, the optimum value of styrene conversion was greater when TS method was applied.

Hydrograph-based storm sewer design optimization by genetic algorithm

2006 ◽  
Vol 33 (3) ◽  
pp. 319-325 ◽  
Author(s):  
M H Afshar ◽  
A Afshar ◽  
M A Mariño ◽  
A A.S Darbandi
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Environmental Protection Agency ◽  
Optimal Solution ◽  
Storm Water ◽  
Storm Water Management Model ◽  
Decision Variables ◽  
Proposed Model ◽  
The Us ◽  
Hydraulic Simulator

A model is developed for the optimal design of storm water networks. The model uses a genetic algorithm (GA) as the search engine and the TRANSPORT module of the US Environmental Protection Agency storm water management model version 4.4H (SWMM4.4H) as the hydraulic simulator. Two different schemes are used to formulate the problem with varying degrees of success in reaching a near-optimal solution. In the first scheme, the nodal elevations and pipe diameters are selected as the decision variables of the problem which were determined by the GA to produce the trial solutions. In the second scheme, only nodal elevations are optimized by the GA, and determination of pipe diameters is left to the TRANSPORT SWMM module. Simulation of the trial solutions in both methods is carried out by the TRANSPORT module of SWMM4.4H. The proposed model is applied to some benchmark examples, and the results are presented and compared with the existing results in the literature.Key words: genetic algorithm, optimal design, sewer network, SWMM.

Optimization of Conventional Rule Curves Coupled with Hedging Rules for Reservoir Operation

2014 ◽  
Vol 140 (5) ◽  
pp. 693-698 ◽  
Author(s):  
Mehrdad Taghian ◽  
Dan Rosbjerg ◽  
Ali Haghighi ◽  
Henrik Madsen
Keyword(s):  
Reservoir Operation ◽  
Rule Curves ◽  
Hedging Rules

Thermodynamic Modeling and Optimization of Cogeneration Heat and Power System Using Evolutionary Algorithm (Genetic Algorithm)

2010 ◽  
Author(s):  
P. Ebrahimi ◽  
H. Karrabi ◽  
S. Ghadami ◽  
H. Barzegar ◽  
S. Rasoulipour ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Combustion Chamber ◽  
Objective Function ◽  
Evolutionary Algorithm ◽  
Gas Turbine ◽  
Saturated Steam ◽  
Design Parameters ◽  
Inlet Temperature ◽  
Decision Variables ◽  
Isentropic Efficiency

A gas-turbine cogeneration system with a regenerative air preheater and a single-pressure exhaust gas boiler serves as an example for application of CHP Plant. This CHP plant which can provide 30 MW of electric power and 14kg/s saturated steam at 20 bars. The plant is comprised of a gas turbine, air compressor, combustion chamber, and air pre-heater as well as a heat recovery steam generator (HRSG). The design Parameters of the plant, were chosen as: compressor pressure ratio (rc), compressor isentropic efficiency (ηac), gas turbine isentropic efficiency (ηgt), combustion chamber inlet temperature (T3), and turbine inlet temperature (T4). In order to optimally find the design parameters a thermoeconomic approach has been followed. An objective function, representing the total cost of the plant in terms of dollar per second, was defined as the sum of the operating cost, related to the fuel consumption. Subsequently, different pars of objective function have been expressed in terms of decision variables. Finally, the optimal values of decision variables were obtained by minimizing the objective function using Evolutionary algorithm such as Genetic Algorithm. The influence of changes in the demanded power on the design parameters has been also studied for 30, 40 MW of net power output.

scholarly journals Application of particle swarm optimization and genetic algorithm for optimization of a southern Iranian oilfield

2021 ◽  
Vol 11 (4) ◽  
pp. 1781-1796
Author(s):  
Milad Razghandi ◽  
Aliakbar Dehghan ◽  
Reza Yousefzadeh
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Simultaneous Optimization ◽  
Injection Wells ◽  
Swarm Optimization ◽  
Operational Conditions ◽  
Water Alternating Gas ◽  
Decision Variables

AbstractOptimization of the placement and operational conditions of oil wells plays an important role in the development of the oilfields. Several automatic optimization algorithms have been used by different authors in recent years. However, different optimizers give different results depending on the nature of the problem. In the current study, a comparison between the genetic algorithm and particle swarm optimization algorithms was made to optimize the operational conditions of the injection and production wells and also to optimize the location of the injection wells in a southern Iranian oilfield. The current study was carried out with the principal purpose of evaluating and comparing the performance of the two most used optimization algorithms for field development optimization on real-field data. Also, a comparison was made between the results of sequential and simultaneous optimization of the decision variables. Net present value of the project was used as the objective function, and the two algorithms were compared in terms of the profitability incremental added to the project over twelve years. First, the production rate of the producers was optimized, and then water alternating gas injection wells were added to the field at locations determined by engineering judgment. Afterward, the location, injection rate, and water alternating gas ratio of the injectors were optimized sequentially using the two algorithms. Next, the production rate of the producers was optimized again. Finally, a simultaneous optimization was done in two manners to evaluate its effect on the optimization results: simultaneous optimization of the last two steps and simultaneous optimization of all decision variables. Results showed the positive effect of the algorithms on the profitability of the project and superiority of the particle swarm optimization over the genetic algorithm at every stage. Also, simultaneous optimization was beneficial at finiding better results compared to sequential optimization approach. In the end, a sensitivity analysis was made to specify the most influencing decision variable on the project’s profitability.

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