Operation Of Multireservoir System Research Articles

A combination approach for optimization operation of multi-objective cascade reservoir systems (Case study: Karun reservoirs)

ABSTRACT Multi-reservoir systems that have diverse and conflicting objectives are challenging to design due to their uncertainties, non-linearities, dimensions and conflicts. The operation of multi-reservoir systems is crucial to increasing hydropower production. In this study, we have investigated the application and effectiveness of the new optimization algorithm MOAHA in multi-objective cascade reservoirs with conflicting objectives, and it has been investigated on a case-by-case basis on Karun cascade reservoirs (Karun 3, Karun 1, Masjed Soleyman and Gotvand). The suggested method (MOAHA) output with other optimization algorithms, MOALO, MOGWO and NSGA-II, were compared and evaluation criteria were used to select the best performance. Additionally, we employed the powerful TOPSIS method to determine the most suitable algorithm. The considered restrictions have also been observed. The results indicate that MOAHA's proposed method is better than the compared algorithms in solving optimal reservoir utilization problems in multi-reservoir water resource systems. The reduction of evaporation (losses) from the tank surface by 9% is accompanied by a 15% increase in hydropower energy production. MOAHA, scoring 0.90, is deemed the best algorithm in this study, whereas MOGWO, with a score of 0.10, is regarded as the least effective algorithm.

Boosting Progressive Optimality Algorithm Performance in Optimizing Complex Large-Scale Multi-Reservoir System Operations by Using Discrepant Optimization Windows and Disturbance-Response Strategy

Boosting Progressive Optimality Algorithm Performance in Optimizing Complex Large-Scale Multi-Reservoir System Operations by Using Discrepant Optimization Windows and Disturbance-Response Strategy

Introducing improved atom search optimization (IASO) algorithm: Application to optimal operation of multi-reservoir systems

Nowadays, optimal reservoir operation is regarded as one of the most important issues in water resources management. In this regard, several optimization algorithms have been used to evaluate the issue of optimal reservoir operation. To increase the accuracy and proximity of the solutions to the absolute optimal solution, the improved atom search optimization (IASO) algorithm was introduced with a mutation strategy to improve its global search capability, as an important factor in preventing trapping in a locally optimal solution. This article implemented the atom search optimization (ASO) algorithm and its improved model to improve the results of the objective function in multi-reservoir systems. Comparing the IASO algorithm with the basic ASO algorithm on the mathematical benchmark functions revealed good accuracy and the superiority of the IASO algorithm. Then, sensitivity analysis was performed on the effective coefficients of both algorithms, in which alpha and beta, as the depth and weight coefficients used in the calculation of atoms acceleration, were considered 50 and 0.5, respectively. The results showed the relatively good accuracy of the ASO algorithm in the case of multi-reservoir systems, but implementing and comparing the results of the improved algorithm, showed improvement so that the best IASO results were improved from 95.33% to 99.99% as close to the global four-reservoir system response of 308.29 and from 89.67 to 94.36% with the solution of the ten-reservoir system in the amount of 1194.44.

Dynamic Self-Adaptive Modeling for Real-Time Flood Control Operation of Multi-Reservoir Systems

In the real-time flood control operation of multi-reservoir systems, it is of great significance to establish a dynamic operating system with high efficiency based on the spatiotemporal variation of flood control situations. This paper proposes a self-adaptive modeling framework for real-time flood control operation of multi-reservoirs based on the cyber–physical system (CPS) theory. Firstly, the random flood samples considering the randomness of both space and magnitude are generated, and then the multi-reservoir real-time flood control hybrid operation (MRFCHO) model is established based on the dynamic identification of effective reservoirs. Then, the CPS theory is introduced to put forward the multi-reservoir real-time flood control hybrid operation cyber–physical system (MRFCHOCPS), which integrates real-time monitoring, control center, database, computation module, and communication network. Finally, the proposed framework is demonstrated in terms of accuracy, efficiency, and adaptability in real-time flood control operations. A case study of the multi-reservoir system upstream of the Lutaizi point in the Huaihe River basin in China reveals that (1) the equivalent qualified rate of the MRFCHO model is 84.9% for random flood samples; (2) the efficiency of solving the MRFCHO model is much higher than the efficiency of solving the MRFCJO model under the premise of ensuring the flood control effect, so it provides a reliable method for the real-time operation of basin-wide floods; (3) the MRFCHOCPS has good adaptability in real-time dynamic modeling and operation of large-scale multi-reservoir systems.

Comprehensive assessment of 20 state-of-the-art multi-objective meta-heuristic algorithms for multi-reservoir system operation

Optimal operation of multi-purpose multi-reservoir dams is a challenging problem for dams’ stakeholders and decision-makers. Optimization algorithms are able to present reliable solutions for such complex problems. This study compared the capability of 20 state-of-the-art robust meta-heuristic algorithms for determining the optimal operating policy of Halilrood multi-reservoir system under the three competing operational objectives of water supply, flood control, and hydropower generation. Before that, the performance of the algorithms in solving two benchmark problems (the Schaffer problem and the MMF1 problem from the CEC suite) was evaluated. Four metrics of generational distance (GD), spacing (S), spread (Δ), and maximum spread (MS) were used to compare the algorithms’ performance in solving the benchmark problems. In addition, four reservoirs performance evaluation indicators of reliability (Rel), resiliency (Res), vulnerability (Vul), and sustainability index (SI) were used to ass the algorithms’ performance in solving the real-case Halilrood problem. The results showed that although the solving capability of each algorithm depends on the nature of the problem, some algorithms were always superior in solving all three problems (2 benchmark problems and one real case). For the Schaffer benchmark problem, the MOAHA algorithm with the performance metrics of (GD = 0.00095, S = 0.58155, Δ = 0.20375, MS = 4.0002) had the best Pareto front in terms of coverage and diversity, and so it was placed at the first rank. In this problem, the MOCryStAl algorithm with (GD = 0.00141, S = 0.48395, Δ = 1.49467, MS = 3.94799) placed at the lowest rank. For the MMF1 problem, the MOMA (GD = 0.00751, S = 0.07516, Δ = 0.29519, MS = 0.99826) followed by the MOAHA (GD = 0.00833, S = 0.07660, Δ = 0.17832, MS = 1.00) had the best performance, respectively, and the MOFA algorithms was the worst. For the Halilrood real-case problem, the MOGWO algorithm (with Rel = 83.41, Res = 67.57, Vul = 22.07 and SI = 76.01) followed by the MOAHA algorithm (Rel = 84.57, Res = 64.71, Vul = 33.60, and SI = 71.41) and the MOMA algorithm (Rel = 79.82, Res = 66.67, Vul = 21.65, and SI = 74.70) obtained the best ranks in terms of reservoir's performance evaluation indicators. They could successfully minimize all three objectives of the total deficit, the flood control and electricity generation indicating the highest performance of these algorithms in the optimization problem. The Pareto front obtained by these algorithms had better distribution and was closer to the origin of coordinates compared to the other utilized algorithms. They could favorably supply the total water demand in the Halilrood basin while increasing the hydropower energy generation up to the power plant capacity at the Jiroft dam. The results of this study can be applied to the optimal operation of any other multi-reservoir system with conflict in water demands.

Application of MOMSA algorithm for optimal operation of Karun multi objective multi reservoir dams with the aim of increasing the energy generation

Multi-reservoir system operation is a challenging and complex task, because of the curse of uncertainties, nonlinearities, dimensionalities and conflicts among the various contradictory objectives. Contemporaneous optimization of such competing objectives needs improving the capability of existing optimization algorithms. This study reports the first application of the recently-introduced multi-objective moth swarm algorithm (MOMSA) in optimization of long-term operation of the large Karun multi-purpose multi-reservoir system. In this algorithm, the decision variables were defined in such a way that the algorithm synchronization capability has improved. Furthermore, a robust mechanism (crowding distance mechanism) with high exploratory capability was employed to select the most efficient solutions within the population. To evaluate the algorithm's efficiency, the results of MOMSA were compared with two well-known multi-objective algorithms of the non-dominated sorting genetic algorithm-II (NSGA-II) and the strength Pareto evolutionary algorithm-II (SPEA-II). To compare the capability of these algorithms, a set of statistical indices including the generational distance (GD), the spacing (S), the spread (Δ), and the maximum spread (MS), along with four reservoir's performance evaluation indicators of the reliability (Rel), the vulnerability (Vul), the resiliency (Res), and the sustainability index were employed. The results demonstrated the superior performance of the MOMSA in solving the Karun multi-reservoir system problem with GD = 259.64, S = 3574.69, Δ = 0.755 and MS = 18420.8. The MOMSA, with the optimized average annual energy production of 15669.7 GW, outperformed the NSGA-II (7569.1 GW) and SPEA-II (9026.3 GW) algorithms. Furthermore, the developed algorithm could supply the downstream demands with a sustainability index of 86.73%, while the corresponding values for the NSGA-II, the SPEA-II and the real operating conditions were 22.73%, 46.09% and 78.87%, respectively. In addition, among the utilized algorithms, the MOMSA demonstrated the best results for flood control purpose. The superior performance of MOMSA will ensure it as a robust algorithm in the optimization of complex and large-scale problems.

Improved progressive optimality algorithm and its application to determination of optimal release trajectory of long-term power generation operation of cascade reservoirs

Dynamic programming (DP) is an effective method for solving multi-stage decision-making problems and has been extensively applied to the optimization of reservoir operations. However, the method is limited by the dimensionality problem; hence, it cannot be directly applied to optimizing the operations of a reservoir system with more than three reservoirs at current computing power. The progressive optimality algorithm (POA) is a classic variant of DP and has been widely used for optimizing multi-reservoir operations. Despite the use of a static variable decoupling strategy to ease DP's dimensionality problem, the POA's performance is reduced by an inherent drawback of the blind search of the static variable decoupling strategy and the dimensionality problem in two-stage solutions. To enhance the POA's performance, we propose an improved version of the POA known as the dipole optimization procedure (DOP) for optimizing cascade reservoir operations. In the improved algorithm, we use a dynamic variable decoupling strategy to obtain search directions that are more targeted than those of the POA so as to improve the quality of the solutions. The dynamic decoupling of variables was achieved by constructing and solving a dipole optimization problem using a DP model developed in this study. Moreover, a perturbation mechanism was introduced to address the POA's dimensionality problem so that the algorithm can be extended to larger reservoir systems. The results of the simulation of a hypothetical four-reservoir system and a real-world cascade reservoir system showed the superiority of the DOP over the POA and other five available alternatives. The comparison was based on the quality of solutions and solving efficiency. The results indicate that the DOP is rational and feasible and has the potential to be applied to optimizing the operation of large-scale multi-reservoir systems with numerous reservoirs.

A new joint optimization method for design and operation of multi-reservoir system considering the conditional value-at-risk

The joint design and operation of multi-reservoir systems is a vital issue for reservoir management. Existing studies mostly focus on determining the optimal scheme by establishing an optimization model and relying on intelligent algorithms. However, the research on the mutual feedback mechanism between the flood control capacity of each reservoir has not been adequately addressed. The overall aim of this paper is to propose a new joint optimization method for design and operation of multi-reservoir systems considering the conditional value-at-risk (CVaRα). In the proposed method, the flood damage assessment with CVaRα for a multi-reservoir system is constructed firstly, and then the feasible flood storage combination scheme (FSCS) of reservoirs in the system is deduced. Finally, the tradeoffs between the hydropower generation benefit and flood damage loss have been analyzed. Selecting China’s Ankang-Danjiangkou Cascade Reservoirs as a case study, the results indicate that (1) the CVaRα value is more sensitive to changes in the flood storage of Danjiangkou reservoir (larger and downstream) than that of Ankang reservoir (smaller and upstream); (2) the feasible area of the FSCSs can be described as a triangle, and the boundary of this feasible interval is determined by the allowable minimum flood storage (AMFS) value of each reservoir; and (3) the relationship between the hydropower generation benefit and the flood damage assessment index CVaRα is not monotonic. These findings are helpful for understanding the relationship between flood storage values of each reservoir from the perspective of the entire multi-reservoir system.

Optimal operation of multi-reservoir system for flood control and hydroelectric generation

In this research, a flood control operating strategy is developed based on a simulation-optimization model to reduce flood damage downstream of multi-reservoir systems by using spillway gates. For this purpose, an optimization algorithm is introduced, in which the maximum water level of the downstream control points and hydropower potential generation in the flood season are objective functions, and the level of the stages of spillway gates are the decision variables. A global optimization tool, Shuffled Complex Evolution (SCE) algorithm implemented in the AUTOCAL software was coupled with the Mike 11 from DHI simulation model for optimizing stages level of spillway gates. As a case study, the Vu Gia Thu Bon rivers catchment including multi-reservoirs of A Vuong, Song Tranh 2, Dak Mi 4 and Song Bung 4 is analyzed. The proposed method was demonstrated to provide an effective tradeoff between flood control and hydropower generation.

Optimal Reservoir Operation of Multi-Reservoir System Using Conditional Atom Search Optimization

Optimal Reservoir Operation of Multi-Reservoir System Using Conditional Atom Search Optimization

Application of Cellular Automata in Bi-Objective Operation of Multi Reservoir Systems

Optimal operation of multi-reservoir systems is one the most challenging problems in water resource management due to their multi-objective nature and time-consuming solving process. In this paper, Multi-Reservoir Parallel Cellular Automata-Simulated Annealing (MPCA-SA), a hybrid method based on cellular automata and simulated annealing is presented for solving bi-objective operations of multi-reservoir systems problems. The problem considers the bi-objective operation of a multi-reservoir system with the two conflicting objectives of water supply and hydropower generation. The MPCA-SA method uses two single-objective cellular automata acting in parallel to explore the problem search space and find the optimal solutions based on the probabilistic interaction with each other. Bi-objective operation of the Dez-Gotvand-Masjed Soleyman three-reservoir system, as a real-world system in southwestern Iran for a period of 60 months, is considered in order to evaluate the ability of the proposed method. In addition, a Non-dominated Sorting Genetic Algorithm (NSGAII) is also used to solve the problems and the results are compared with those of MPCA-SA, indicating the capabilities of the proposed MPCA-SA method. The results show that the MPCA-SA method is able to produce solutions comparable to those of NSGAII with a much-reduced computational cost equal to 1.2% of that required by the NSGAII, emphasizing the efficiency and practicality of the proposed method.

Optimal operation of single and multi-reservoir systems via hybrid shuffled grey wolf optimization algorithm (SGWO)

Abstract Reservoir operation is a key issue in the water resources system. In this paper, the Shuffled Grey Wolf Optimizer (SGWO), a hybrid optimization algorithm inspired by Shuffled Complex Evolution (SCE-UA) and Gray Wolf Optimizer (GWO) algorithms, is introduced. The main modification in the proposed algorithm is how it divides and shuffles the population to enhance the information exchange among the individuals. The performance of the SGWO algorithm is compared to famous evolutionary algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in solving mathematical benchmark functions and multiple types of reservoir operation optimization problems with different scales. Two hypothetical 4 and 10-reservoir system, and the Dez dam in Iran as a single reservoir system were selected as the case study in this research. The capability of the algorithms was compared in terms of accuracy of derived optimum objective function values, convergence speed, and stability of answers in different implementations. The results showed that the SGWO can reach considerably better results (0.3% to 26% better than the closest rival algorithms) using significantly lower number of function evaluations. It also showed the lowest standard deviation among other algorithms for all problems, which indicated the high reliability of this algorithm.

An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.

Integrated Operation of Multi-Reservoir and Many-Objective System Using Fuzzified Hedging Rule and Strength Pareto Evolutionary Optimization Algorithm (SPEA2)

In this paper, a many-objective optimization algorithm was developed using SPEA2 for a system of four reservoirs in the Karun basin, including hydropower, municipal and industrial, agricultural, and environmental objectives. For this purpose, using 53 years of available data, hedging rules were developed in two modes: with and without applying fuzzy logic. SPEA2 was used to optimize hedging coefficients using the first 43 years of data and the last 10 years of data were used to test the optimized rule curves. The results were compared with those of non-hedging methods, including the standard operating procedures (SOP) and water evaluation and planning (WEAP) model. The results indicate that the combination of fuzzy logic and hedging rules in a many-objectives system is more efficient than the discrete hedging rule alone. For instance, the reliability of the hydropower requirement in the fuzzified discrete hedging method in a drought scenario was found to be 0.68, which is substantially higher than the 0.52 from the discrete hedging method. Moreover, reduction of the maximum monthly shortage is another advantage of this rule. Fuzzy logic reduced 118 million cubic meters (MCM) of deficit in the Karun-3 reservoir alone. Moreover, as expected, the non-hedging SOP and WEAP model produced higher reliabilities, lower average storages, and less water losses through spills.

Optimal operation of multi-reservoir systems for increasing power generation using a seagull optimization algorithm and heading policy

Power supply is a key issue for decision-makers. The reservoir operation of multi-reservoir systems is an important aspect to consider in efforts to increase power generation. This research studies a multi-reservoir system comprising of the Khersan-I (KHI), Karoon-III (KAIII) and Karoon-IV (KAIV) with the intent being to increase power generation. To achieve this, the Two-Point Heading Rule was integrated with a new optimization algorithm, namely the Seagull Optimization Algorithm (SEOA). The Two-Point Heading Rule was used based on four distinct scenarios, namely Two-Point Heading Rule (1), Two-Point Heading Rule (2), Two-Point Heading Rule (3) and Two-Point Heading Rule (4). The Seagull Optimization Algorithm was then used to find two heading parameters of the TPHRs. The Seagull Optimization Algorithm was subsequently benchmarked against the Salp Swarm Algorithm (SSA), Bat Algorithm (BA) and the Shark Optimization Algorithm (SOA). Various inflow scenarios consisting of the first inflow scenario (dry condition), the second inflow scenario (normal) and the third inflow scenario (wet condition) were considered for the optimal operation of this multi-reservoir system. The results indicated that the global solution of the MSOO based on NLP for Two-Point Heading Rule (1) under the first inflow scenario and was 3.22 while the average solution of Seagull Optimization Algorithm, Salp Swarm Algorithm, Shark Optimization Algorithm, and Bat Algorithm in respective order was 3.25, 3.93, 4.87 and 6.03. The results indicated that the global solution of the MSOO based on NLP for Two-Point Heading Rule (1) under the second inflow scenario was 2.14 while the average best solution of Seagull Optimization Algorithm, Salp Swarm Algorithm, Shark Optimization Algorithm, and Bat Algorithm in respective order was 2.16, 2.98, 3.96, and 4.89. It can be concluded that the SEOA outperformed all of the other algorithms. It was also found that the SEOA based on the Two-Point Heading Rule (3) under the third inflow scenario provided the most power generation for the KHI and KAIV systems. A multi-criteria decision was utilized to choose the best algorithm and heading policy. The ensuing results indicate that the SEOA had the best performance out of all the algorithms based on Two-Point Heading Rule (3) and the third inflow scenario.

Optimal Operation of a Parallel Multireservoir System for Flood Control using a Stagewise Compensation Method

Optimal operation of multireservoir flood control systems is a nonlinear and high-dimensional problem. For a parallel multireservoir system, the impact of joint compensation flood control operation for each reservoir on reducing the peak flow downstream should be considered. This study proposed an optimization method called the stagewise compensation method (SWCM) for flood control operation of a parallel multireservoir system based on the concept of three-stage flood control operation and compensation flood control operation. The dynamic programming-progressive optimality algorithm (DP-POA) was also implemented to verify the solutions of SWCM. The Ji’an parallel multireservoir system was selected as a case study. The results show that SWCM can decompose a parallel multireservoir system into a single-reservoir system and consider the impact of the joint compensation operation for each reservoir on reducing peak flow in the downstream area. When applying SWCM, the reduction rate of typical and 10-year, 20-year, 30-year, 50-year and 100-year designed flood peaks at Ji’an hydrological station can reach over 12% on average, which is great progress compared with conventional flood control operation rules and DP-POA.

Multiple Hydropower Reservoirs Operation by Hyperbolic Grey Wolf Optimizer Based on Elitism Selection and Adaptive Mutation

Multiple hydropower reservoirs operation is an effective measure to rationally allocate the limited water resources under uncertainty. With the rapid expansion of water resources system, it becomes much more difficult for traditional methods to quickly yield the reasonable operational policy. Grey wolf optimizer, inspired by the wolves’ hunting behaviors, is a famous metaheuristic method to resolve engineering optimization problems, but still suffers from the local convergence and search stagnation defects. To alleviate this problem, this study proposes a hybrid grey wolf optimizer (HGWO) where the hyperbolic accelerating strategy is introduced to improve the local search ability; the adaptive mutation strategy is used to diversify the swarm; the elitism selection strategy is used to enhance the convergence speed. The experimental results show that the HGWO method can produce better solutions than its original version in several test functions. Then, the HGWO method is applied to resolve the optimal operation of a real-world hydropower system with the goal of maximizing the total generation benefit. The simulations indicate that the HGWO method produces satisfying scheduling schemes than several control methods in terms of all the statistical indicators. Hence, with the merits of superior search ability, rapid convergence rate and gradient information avoidance, HGWO proves to be a promising alternative optimization tool for the complex multireservoir system operation problem.

Optimal operation of multi-reservoir systems: comparative study of three robust metaheuristic algorithms

Abstract In this study, the capability of the recently introduced moth swarm algorithm (MSA) was compared with two robust metaheuristic algorithms: the harmony search (HS) algorithm and the imperialist competitive algorithm (ICA). First, the performance of these algorithms was assessed by seven benchmark functions having 2–30 dimensions. Next, they were compared for optimization of the complex problem of four-reservoir and 10-reservoir systems operation. Furthermore, the results of these algorithms were compared with nine other metaheuristic algorithms. Sensitivity analysis was performed to determine the appropriate values of the algorithms’ parameters. The statistical indices coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), normalized MSE (NMSE), mean absolute percentage error (MAPE), and Willmott’s index of agreement (d) were used to compare the algorithms’ performance. The results showed that MSA was the superior algorithm for solving all benchmark functions in terms of obtaining the optimal value and saving CPU usage. ICA and HS were ranked next. When the dimensions of the problem were increased, the performance of ICA and HS dropped but MSA has still performed extremely well. In addition, the minimum CPU usage and the best solutions for the optimal operation of the four-reservoir system were obtained by MSA, with values of 269.7 seconds and 308.83, which are very close to the global optimum solution. Corresponding values for ICA were 486.73 seconds and 306.47 and for HS were 638.61 seconds and 264.61, which ranked them next. Similar results were observed for the 10-reservoir system; the CPU time and optimal value obtained by MSA were 722.5 seconds and 1,195.58 while for ICA they were 1,421.62 seconds and 1,136.22 and for HS they were 1,963.41 seconds and 1,060.76. The R2 and RMSE values achieved by MSA were 0.951 and 0.528 for the four-reservoir system and 0.985 and 0.521 for the 10-reservoir system, which demonstrated the outstanding performance of this algorithm in the optimal operation of multi-reservoir systems. In a general comparison, it was concluded that among the 12 algorithms investigated, MSA was the best, and it is recommended as a robust and promising tool in the optimal operation of multi-reservoir systems.

Verification of FPA and PSO algorithms for rule curve extraction and optimization of single- and multi-reservoir systems' operations considering their specific purposes

Abstract The optimal operation of reservoirs is known as a complex issue in water resources management, which requires consideration of numerous variables (such as downstream water demand and power generation). For this optimization, researchers have used evolutionary and meta-heuristic algorithms, which are generally inspired by nature. These algorithms have been developed to achieve optimal/near-optimal solutions by a smaller number of function evaluations with less calculation time. In this research, the flower pollination algorithm (FPA) was used to optimize: (1) Aidoghmoush single-reservoir system operation for agricultural water supply, (2) Bazoft single-reservoir system operation for hydropower generation, (3) multi-reservoir system operation of Karun 5, Karun 4, and Bazoft, and (4) Bazoft single-reservoir system for rule curve extraction. To demonstrate the effectiveness of the FPA, it was first applied to solve the mathematical test functions, and then used to determine optimal operations of the reservoir systems with the purposes of downstream water supply and hydropower generation. In addition, the FPA was compared with the particle swarm optimization (PSO) algorithm and the non-linear programming (NLP) method. The results for the Aidoghmoush single-reservoir system showed that the best FPA solution was similar to the NLP solution, while the best PSO solution was about 0.2% different from the NLP solution. The best values of the objective function of the PSO were approximately 3.5 times, 28%, and 43% worse than those of the FPA for the Bazoft single-reservoir system for hydropower generation, the multi-reservoir system, and the Bazoft single-reservoir system for rule curve extraction, respectively. The FPA outperformed the PSO in finding the optimal solutions. Overall, FPA is one of the new evolutionary algorithms, which is capable of determining better (closer to the ideal solution) objective functions, decreasing the calculation time, simplifying the problem, and providing better solutions for decision makers.

Study on Multi-Objective Operation Strategy for Multi-Reservoirs in Small-Scale Watershed Considering Ecological Flows

The operation of multi-reservoir system has significantly influence on ecological system in riverine by altering natural streamflow. To alleviate the negative impacts on the ecosystem in a small-scale watershed, ecological flow requirements (EFRs) are incorporated in the operating strategies to tradeoff between the existing human needs and EFRs. In this study, three scenarios of EFRs are firstly generated to study the impacts on the operation efficiency of the existing objectives. Then, the reservoirs are classified into three categories, and the operation strategies are defined, respectively. Finally, the operation strategies are optimized using NSGA-II algorithm and used to simulate the operation processes. The simulation results indicate that the operation objectives between the EFRs and agricultural irrigation have a significant competitive relationship, and the guarantee rate for agricultural irrigation decreases with the EFRs increasing. The reservoirs in the third category with large storage capacity have strong regulation ability for the EFRs, however the reservoir operations in other categories are more vulnerable in term of guarantee rate for irrigation.