This paper aims to improve summer power generation of the Yeywa Hydropower Reservoir in Myanmar using the modified multi-step ahead time-varying hedging (TVH) rule as a case study. The results of the TVH rules were compared with the standard operation policy (SOP) rule, the binary standard operation policy (BSOP) rule, the discrete hedging (DH) rule, the standard hedging (SH) rule, the one-point hedging (OPH) rule, and the two-point hedging (TPH) rule. The Multi-Objective Genetic Algorithm (MOGA) was utilized to drive the optimal Pareto fronts for the hedging rules. The results demonstrated that the TVH rules had higher performance than the other rules and showed improvements in power generation not only during the summer period but also over the entire period.
相似文献To satisfy their main goal, namely providing quality water to consumers, water distribution networks (WDNs) need to be suitably monitored. Only well designed and reliable monitoring data enables WDN managers to make sound decisions on their systems. In this belief, water utilities worldwide have invested in monitoring and data acquisition systems. However, good monitoring needs optimal sensor placement and presents a multi-objective problem where cost and quality are conflicting objectives (among others). In this paper, we address the solution to this multi-objective problem by integrating quality simulations using EPANET-MSX, with two optimization techniques. First, multi-objective optimization is used to build a Pareto front of non-dominated solutions relating contamination detection time and detection probability with cost. To assist decision makers with the selection of an optimal solution that provides the best trade-off for their utility, a multi-criteria decision-making technique is then used with a twofold objective: 1) to cluster Pareto solutions according to network sensitivity and entropy as evaluation parameters; and 2) to rank the solutions within each cluster to provide deeper insight into the problem when considering the utility perspectives.The clustering process, which considers features related to water utility needs and available information, helps decision makers select reliable and useful solutions from the Pareto front. Thus, while several works on sensor placement stop at multi-objective optimization, this work goes a step further and provides a reduced and simplified Pareto front where optimal solutions are highlighted. The proposed methodology uses the NSGA-II algorithm to solve the optimization problem, and clustering is performed through ELECTRE TRI. The developed methodology is applied to a very well-known benchmarking WDN, for which the usefulness of the approach is shown. The final results, which correspond to four optimal solution clusters, are useful for decision makers during the planning and development of projects on networks of quality sensors. The obtained clusters exhibit distinctive features, opening ways for a final project to prioritize the most convenient solution, with the assurance of implementing a Pareto-optimal solution.
相似文献Subsurface dams, strongly advocated in the 1992 United Nations Agenda-21, have been widely studied to increase groundwater storage capacity. However, an optimal allocation of augmented water with the construction of the subsurface dams to compensate for the water shortage during dry periods has not so far been investigated. This study, therefore, presents a risk-based simulation–optimization framework to determine optimal water allocation with subsurface dams, which minimizes the risk of water shortage in different climatic conditions. The developed framework was evaluated in Al-Aswad falaj, an ancient water supply system in which a gently sloping underground channel was dug to convey water from an aquifer via the gravity force to the surface for irrigation of downstream agricultural zones. The groundwater dynamics were modeled using MODFLOW UnStructured-Grid. The data of boreholes were used to generate a three-dimensional stratigraphic model, which was used to define materials and elevations of five-layer grid cells. The validated groundwater model was employed to assess the effects of the subsurface dam on the discharge of the falaj. A Conditional Value-at-Risk optimization model was also developed to minimize the risk of water shortage for the augmented discharge on downstream agricultural zones. Results show that discharge of the falaj is significantly augmented with a long-term average increase of 46.51%. Moreover, it was found that the developed framework decreases the water shortage percentage in 5% of the worst cases from 87%, 75%, and 32% to 53%, 32%, and 0% under the current and augmented discharge in dry, normal, and wet periods, respectively.
相似文献Linked simulation-optimization (S/O) approaches have been extensively used as tools in coastal aquifer management. However, parameter uncertainties in seawater intrusion (SI) simulation models often undermine the reliability of the derived solutions. In this study, a stochastic S/O framework is presented and applied to a real-world case of the Longkou coastal aquifer in China. The three conflicting objectives of maximizing the total pumping rate, minimizing the total injection rate, and minimizing the solute mass increase are considered in the optimization model. The uncertain parameters are contained in both the constraints and the objective functions. A multiple realization approach is utilized to address the uncertainty in the model parameters, and a new multiobjective evolutionary algorithm (EN-NSGA2) is proposed to solve the optimization model. EN-NSGA2 overcomes some inherent limitations in the traditional nondominated sorting genetic algorithm-II (NSGA-II) by introducing information entropy theory. The comparison results indicate that EN-NSGA2 can effectively ameliorate the diversity in Pareto-optimal solutions. For the computational challenge in the stochastic S/O process, a surrogate model based on the multigene genetic programming (MGGP) method is developed to substitute for the numerical simulation model. The results show that the MGGP surrogate model can tremendously reduce the computational burden while ensuring an acceptable level of accuracy.
相似文献Traditional multi-objective evolutionary algorithms treat each objective equally and search randomly in all solution spaces without using preference information. This might reduce the search efficiency and quality of solutions preferred by decision makers, especially when solving problems with complicated properties or many objectives. Three reference point based algorithms which adopt preference information in optimization progress, e.g., R-NSGA-II, r-NSGA-II and g-NSGA-II, have been shown to be effective in finding more preferred solutions in theoretical test problems. However, more efforts are needed to test their effectiveness in real-world problems. This study conducts a comparison of the above three algorithms with a standard algorithm NSGA-II on a reservoir operation problem to demonstrate their performance in improving the search efficiency and quality of preferred solutions. Under the same calculation times of the objective functions, Pareto optimal solutions of the four algorithms are used in the empirical comparison in terms of the approximation to the preferred solutions. Three performance indicators are then adopted for further comparison. Results show that R-NSGA-II and r-NSGA-II can improve the search efficiency and quality of preferred solutions. The convergence and diversity of their solutions in the concerned region are better than NSGA-II, and the closeness degree to the reference point can be increased by 42.8%, and moreover the number of preferred solutions can be increased by more than 3 times when part of objectives are preferred. By contrast, g-NSGA-II shows worse performance. This study exhibits the performance of three reference point based algorithms and provides insights in algorithm selection for multi-objective reservoir optimization problems.
相似文献The present study’s main objective is to simultaneously minimize operational and seepage losses in agricultural water distribution systems, relying on the Ant Colony Optimization (ACO). For this purpose, the following arrangements were made: i) Hydraulic flow simulation of the distribution systems was conducted by developing an Integrator Delay (ID) model using MATLAB and appraisal performance of the water distribution system, ii) The seepage simulation alongside the system employing a calibrated and validated estimation equation, iii) developing the ACO model to minimize operational and seepage losses within the agricultural water distribution Systems. Two single-objective and one multi-objective functions were considered to minimize seepage loss, operational loss, and both loss components simultaneously. The Moghan irrigation water distribution system, Iran, was selected as the case study. Optimization results revealed that the first through third objective functions managed to reduce the total losses in the Moghan water distribution systems by, respectively, 0.39, 3.1, and 4% compared to the existing conditions. A comparison with the optimization results from LINGO, a nonlinear optimization model, was suggestive of the advantages of the ACO in terms of the optimal result and optimization time. The proposed method can be used as a practical measure to improve water productivity within the scale of agricultural water distribution systems by improving the manual operating system’s performance in the status quo.
相似文献Reservoirs are used as one of the surface water sources for different and often conflicting water supply purposes. Given the complex management policies governing a basin, it is essential to simultaneously consider different goals and cope with the associated trade-off in water resources management. This purpose requires coupling a multi-objective optimization algorithm with a reservoir simulation model, which this approach increases required computational efforts. Various simulation–optimization approaches have been developed and used for solving the related problems. However, they often have complicated methods and certain limitations in real-world applications. In this study, a new multi-objective firefly algorithm—K nearest neighbor (MOFA-KNN) hybrid algorithm is developed which is time-efficient and is not as complicated as previous approaches. The proposed algorithm was evaluated for both benchmark and real problems. The results of the benchmark problem showed that the execution time of the MOFA-KNN hybrid algorithm was up to 99.98% less than that of the multi-objective firefly algorithm (MOFA). In the real problem, the MOFA-KNN algorithm was linked to the 2D hydrodynamic and water quality model, CE-QUAL-W2, to test the developed framework for reservoir operation. The Aidoghmoush reservoir as a case study investigated to minimize the total released dissolved solids (TDS) and the water temperature difference between the inflow and the outflow. The results demonstrated that the MOFA-KNN algorithm significantly reduced the simulation–optimization execution time (>?660 times compared with MOFA). The minimum released TDS from the reservoir was 13.6 mg /l and the minimum temperature difference was 0.005 °C.
相似文献The integrated management of water supply and demand has been considered by many policymakers; due to its complexity the decision makers have faced many challenges so far. In this study, we proposed an efficient framework for managing water supply and demand in line with the economic and environmental objectives of the basin. To design this framework, a combination of ANFIS and multi-objective augmented ε-constraint programming models and TOPSIS were used. First, using hydrological data from 2001 to 2017, the rate of water release from the dam reservoir was estimated with the ANFIS model; afterwards, its allocation to agricultural areas was performed by combining multi-objective augmented ε-constraint models and TOPSIS. To prove the reliability of the proposed model, the southern Karkheh basin in Khuzestan province, Iran, was considered as a case study. The results have showed that this model is able to reduce irrigation water consumption and to improve its economic productivity in the basin.
相似文献In this study, the zone-based hedging rule, which is the main operating policy adopted from multipurpose reservoirs in Korea is adjusted to reflect the multi-year droughts caused by climate change. Annual synthetic inflow series with different magnitudes of long memory were generated using the autoregressive fractional integrated moving average (ARFIMA) model. The generated inflow series were then disaggregated into 10-day series and utilized as input variables to derive the alternative hedging rules. The alternative hedging rules from this study were used in adaptive reservoir management by newly updated information. Finally, the performance of the suggested policy is measured in terms of frequency and magnitude under the historical inflow series. As a result, adaptive reservoir management demonstrated improvements in the following terms of the frequency of critical failures (water deficit ratio greater than 30%): 6.14% of the simulation period in the status quo (SQ) policy, and 2.99% in the adaptive management. However, the overall reliability of the reservoir during the simulation horizon was better when operated with the SQ policy (41.19%) than the results from adaptive management (26.42%). Because this result is in a good agreement with the original objective of the hedging rules, the adaptive policy suggested in this study holds promise and may be utilized in further reservoir management with an increase of potential drought risk from climate change.
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