Multi-reservoir System Research Articles

"Comparison Linear Programming and Improving Shuffled Complex Algorithm for Optimizing Multi Reservoir Systems"

"Comparison Linear Programming and Improving Shuffled Complex Algorithm for Optimizing Multi Reservoir Systems"

Stochastic programming for floodwater utilization of a complex multi-reservoir system considering risk constraints

Floodwater utilization through dynamic operating flood limited water levels (FLWLs) of reservoirs plays an important role in flood control and water conservation. This study proposes a risk-based aggregation-decomposition method for floodwater utilization of a complex multi-reservoir system. The original system is decomposed into four basic configurations. For the subsystem with hydraulic connections, the upper bound of FLWL of each reservoir is identified based on a given risk level for the subsystem and risk hedging between reservoirs. A stochastic programming model to maximize hydropower generation is then established for joint operation of the multi-reservoir system to find and update the optimal operation strategy during flood seasons. The mixed cascade reservoirs in the Pi River Basin are selected as a case study. The application results are compared with those of the original design model and deterministic operation model without considering risk, which shows that joint operation and dynamic control of FLWLs: 1) significantly increases the power generation and improves the utilization rate of floodwaters compared to those of the original design model; 2) power production rate increases with the acceptable risk but the increasing rate between them decreases with higher levels of risk; 3) results in lower utilization rate of floodwaters compared to that of the deterministic model, but the difference is not significant. The proposed method provides a framework for risk-based decision-making in floodwater utilization to enhance the comprehensive benefits of a multi-reservoir system.

Minimizing the impact of vacating instream storage of a multi-reservoir system: a trade-off study of water supply and empty flushing

Abstract. A reservoir operator does not favor storage above a certain level in situations such as the pre-release operation prior to a flood; scheduled engineering construction; or mechanical excavation of sediment in the impoundments, drawdown, and empty flushing, etc. This paper selects empty flushing as the case study, and a method is presented to promote the feasibility of emptying the reservoir storage. The impact of emptying reservoir on water supply is minimized through appropriate joint operation in a multi-reservoir system, where drawdown and empty flushing is carried out in a primary reservoir, and the other reservoir provides backup water for supply. This method prioritizes allocating the storage in the primary reservoir for water supply during specific periods prior to its emptying. If the storage of every reservoir achieves its predefined conditions, drawdown of the primary reservoir is activated and followed by empty flushing. Previously preserved storage in the other reservoir ensures adequate water supply during the periods of emptying the primary reservoir. Flushing of the primary reservoir is continued until either the accumulative released water exceeds the specified volume, storage in the backup reservoir drops below the predefined threshold, or the inflow to the primary reservoir recedes from the flood peak to be below the releasing capacity of outlets. This behavior is simulated and linked with a nonlinear optimization algorithm to calibrate the optimal parameters defining the activation and termination of empty flushing. The optimized strategy limits the incremental water shortage within the acceptable threshold and maximizes the expected benefits of emptying reservoir.

Análise do comportamento operacional de reservatórios com redes neurais artificiais: o caso de Luiz Gonzaga, Brasil

Reservoirs are operated following specific policies, constrained by hydrological and structural conditions. When modeling antropized water systems with reservoirs, the incorporation of existing operating policies is important to improve model capability. However, operating policies are not always available or easy to identify within large-scale multi-reservoir systems, where operation derives from large number of variables and constraints rather than a clear-cut local objective function. This study applies Artificial Neural Networks (ANNs) with the objective of analyzing if local variables (inflow, storage level, and evaporation) of a sub-system part of a large-scale coordinated multi-reservoir system are sufficient predictors of the operational behavior (release decisions) in a daily time step. The sub-system includes the Luiz Gonzaga and Sobradinho reservoirs. Results pointed to a Nash–Sutcliffe efficiency coefficient (NS) of 0.67 to 0.74 and a coefficient of determination (r2) of 0.75, showing that we can predict the sub-system operational behavior most of the time but with some outflow peaks under predicted.

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.

Multi-mechanism ensemble interior search algorithm to derive optimal hedging rule curves in multi-reservoir systems

The operating rules, especially the hedging rules, which are known as useful tools for effective utilization of water from reservoir systems, are commonly applied for managing reservoir systems in severe drought periods. Extracting optimal operation policies for multi-reservoir systems is a challenging issue due to their non-linear, large-scale, and stochastic nature. Therefore, improving optimization techniques is a worthy effort in developing the operating rules for multi-reservoir systems. Hence, this research aimed to develop an efficient optimization method called multi-mechanism ensemble interior search algorithm (MEISA) to tackle various problems in the optimal management of reservoir systems. The MEISA promotes the two main operators of the interior search algorithm (ISA) (i.e., the composition and the mirror operators) to enhance the capability of global and local search. Besides, the proposed algorithm employs an enhanced opposition-based learning (EOBL) operator to improve the best solution and increase the convergence speed. The MEISA also uses an adaptive parameter to balance the global and local search in the proposed algorithm. The results of several test functions demonstrate that the MEISA outperforms other optimizers in terms of the solution accuracy, the quality, and the convergence rate. Finally, the results of optimizing the hedging rules for two real-world single- and multi-reservoir systems in Iran indicate that the MEISA is more suitable efficient in ameliorating irreversible impacts of severe drought periods as compared with other optimization methods. The results show that the MEISA has highly convincing performance in obtaining the optimal hedging rules for the efficient management of multi-reservoir systems.

A hybrid constrained coral reefs optimization algorithm with machine learning for optimizing multi-reservoir systems operation

The continuous growing demand for water, prolonged periods of drought, and climatic uncertainties attributed mainly to climate change mean surface water reservoirs more than ever need to be managed efficiently. Several optimization algorithms have been developed to optimize multi-reservoir systems operation, mostly during severe dry/wet seasons, to mitigate extreme-events consequences. Yet, convergence speed, presence of local optimums, and calculation-cost efficiency are challenging while looking for the global optimum. In this paper, the problem of finding an efficient optimal operation policy in multi-reservoir systems is discussed. The complexity of the long-term operating rules and the reservoirs' upstream and downstream joint-demands projected in recursive constraints make this problem formidable. The original Coral Reefs Optimization (CRO) algorithm, which is a meta-heuristic evolutionary algorithm, and two modified versions have been used to solve this problem. Proposed modifications reduce the calculation cost by narrowing the search space called a constrained-CCRO and adjusting reproduction operators with a reinforcement learning approach, namely the Q-Learning method (i.e., the CCRO-QL algorithm). The modified versions search for the optimum solution in the feasible region instead of the entire problem domain. The models’ performance has been evaluated by solving five mathematical benchmark problems and a well-known continuous four-reservoir system (CFr) problem. Obtained results have been compared with those in the literature and the global optimum, which Linear Programming (LP) achieves. The CCRO-QL is shown to be very calculation-cost-effective in locating the global optimum or near-optimal solutions and efficient in terms of convergence, accuracy, and robustness.

A Procedure for Combining Improved Correlated Sampling Methods and a Resampling Strategy to Generate a Multi-Site Conditioned Streamflow Process

In this study, a new method is developed to generate multi-site streamflow series. First, a correlated sampling method based on spectral decomposition is employed to simulate a large number of individual years of multi-site streamflow series. Then, combined with historical design flood data, flood characteristic series considering historical floods are generated. Finally, a subset of the streamflow series corresponding to these flood characteristics is selected from the pool of multi-site simulated streamflow series. The proposed model is applied to multi-site streamflow simulations in the upper Yangtze River basin. Experimental results show that the simulated data not only ensure the fundamental statistical characteristics of the observed data, but also provide good preservation of high order self-dependent, cross-dependent at finer and coarser time scales with any marginal distributions and any correlation structures. In addition, the simulated extreme situations are more reliable for risk and vulnerability analysis of multi-reservoir joint operation systems due to the consideration of the historical design flood data.

Performance Comparison of Equivalent Reservoir and Multireservoir Models in Forecasting Hydropower Potential for Linking Water and Power Systems

AbstractTo link water and power systems on a regional scale, equivalent reservoir models—an aggregated representation of a multireservoir system—are commonly used because conventional river-basin s...

Forensic engineering analysis applied to flood control

Floods have various impacts, including loss of life and damage to property. Flood- management reservoirs can help mitigate floods, but their operation can also worsen flood impacts. This paper presents a novel forensic engineering approach to assess the role of reservoir operation on flood control. Fourteen criteria are employed for assessing forecast-based prereleases of water from reservoir storage to reduce the impact of flooding. The proposed approach is applied for forensic assessment of the system performance of reservoirs during the large flood of 2019 in southwestern Iran (the Great Karun Basin). The two main study areas are in the sub-basins of Karun and Dez. Results concerning two key performance criteria (the peak discharge reduction (PDR) and flood volume reduction (FVR)) show the PDR criterion in the Karun sub-basin multi-reservoir system reached about 79% (where 100% is the theoretically best performance) under historic operations (actual operating conditions in 2019), and improved from 8 to 19% if various prerelease operations were made. The FVR achieved about 33% in the historical situation and improved from 20 to 59% under prerelease operations scenarios, respectively. The PDR criterion achieved 26% under the historical scenario, but with better operation could exceed 55% in the Dez sub-basin multi-reservoir system, whereas FVR was as low as 11% in 2019 but could be raised to between 15 and 25% under prerelease operations. This forensic work’s assessments establish that improved reservoir operation could be achieved by applying specialized operation approaches.

Identifying complex networks and operating scenarios for cascade water reservoirs for mitigating drought and flood impacts

Clearly, water conservancy projects are advantageous for human beings; however, they significantly change the streamflow variability of rivers. This change is a result of the complex and non-linear interactions between natural processes and human activities. Thus far, our understanding of these interactions is still inadequate. In this study, these interactions were evaluated for a large river basin (i.e. the Pearl River basin) with numerous reservoirs to identify reservoir operation scenarios for mitigating drought and flood impacts. To this end, first, the flow variability pattern was derived based on the concept of the admissible range of flow variability. Second, the moving average over shifting horizon method was used to evaluate the effects of the reservoirs on the streamflow variability. Finally, the network theory and data mining technology were applied to establish the streamflow relations between the upstream and downstream reservoirs. The results show that the cascade reservoirs reshape the streamflow variability patterns of the Pearl River in time and space and have a cumulative effect on the downstream runoff. The constructed networks and the identified operation scenarios present that this effect is mainly influenced by the upstream critical reservoirs having different inflows and storage capacities. This suggests the varied roles of the reservoirs for drought mitigation and flood control limiting the formulation and application of integrated operating strategies for the multi-reservoir system.

Global warming impact to River Basin of Blue Nile and the optimum operation of its multi-reservoir system for hydropower production and irrigation

The water resource of the Blue Nile River basin (BNRB) has been under pressure due to growing demands from many users, and the climate change impact. Potential impact of climate change for the maximum, median and minimum projected changes in the simulated streamflow of BNRB by a hydrologic model, VIC, driven by Representative Concentration Pathways climate scenarios, RCP4.5 and RCP8.5, of 4 GCMs (global climate models) downscaled dynamically by a regional climate model, WRF (Weather Research Forecasting) using a one-domain framework that covers the entire NRB for 2041–2070 and 2071–2100. These projected changes in streamflow were used to assess its future water allocations using a stochastic Dual Dynamic Programming (SDDP) algorithm and a hydro-economic model to optimize hydropower production and irrigated agriculture. Overall, it seems the Grand Ethiopian Renaissance Dam (GERD) reservoir will likely not operate at full storage level because the streamflow of BNRB is assumed to be regulated by three upstream reservoirs. The outflow from the reservoir of GERD or BNRB's annual flow at Khartoum is projected to increase under maximum, but is expected to decrease under minimum and median projected changes in streamflow for 2041–2070 and 2071–2100, respectively. Given the annual net benefit obtained from hydropower production and irrigated agriculture of the reservoir is projected to increase (decrease) under the maximum (median and minimum) projected changes in streamflow, the potential climate change impact should be considered in designing and developing the future water resources of BNRB.

Sustainable Multi-Objective Multi-Reservoir Optimization Considering Environmental Flow

Increasing demand for water from all sectors presents a challenge for policy makers to improve water allocation policies for storage reservoirs. In addition, there are many other organisms and species present in river waters that also require water for their survival. Due to the lack of awareness many times the minimum required quantity and quality of water for river ecosystem is not made available at downstream of storage reservoirs. So, a sustainable approach is required in reservoir operations to maintain the river ecosystem with environmental flow while meeting the other demands. Multi-objective, multi-reservoir operation model developed with Python programming using Fuzzy Linear Programing method incorporating environmental flow requirement of river is presented in this paper. Objective of maximization of irrigation release is considered for first run. In second run maximization of releases for hydropower generation is considered as objective. Further both objectives are fuzzified by incorporating linear membership function and solved to maximize fuzzified objective function simultaneously by maximizing satisfaction level indicator (λ). The optimal reservoir operation policy is presented considering constraints including Irrigation release, Turbine release, Reservoir storage, Environmental flow release and hydrologic continuity. Model applied for multi-reservoir system consists of four reservoirs, i.e., Jayakwadi Stage-I Reservoir (R1), Jayakwadi Stage-II Reservoir (R2), Yeldari Reservoir (R3), Siddheshwar Reservoir (R4) in Godavari River sub-basin from Marathwada region of Maharashtra State, India.

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.

Using 2D HEC-RAS Modeling and Embankment Dam Break Scenario for Assessing the Flood Control Capacity of a Multi-Reservoir System (NE Romania)

Using hydraulic modeling techniques (e.g., one-dimensional/two-dimensional (1D/2D) hydraulic modeling, dam break scenarios) for extracting the flood settings is an important aspect of any action plan for dam failure (APDF) and flood mitigation strategy. For example, the flood hydraulic models and dam break scenario generated based on light detection and ranging (LiDAR)-derived digital elevation models (DEMs) and processed in the dedicated geographic information systems (GIS) and hydraulic modeling software (e.g., HEC-RAS—Hydrologic Engineering Center River Analysis System, developed by USACE HEC, Davis, CA, USA) can improve the flood hazard maps in case of potentially embankment dam failure. In this study, we develop a small-scale conceptual approach using 2D HEC-RAS software according to the three embankment dam break scenarios, LiDAR data (0.5 m spatial resolution), and 2D hydraulic modeling for the Başeu multi-reservoir system which belongs to the Başeu River (NE Romania) including R1—Cal Alb reservoir, R2—Movileni reservoirs, R3—Tătărăşeni reservoirs, R4—Negreni reservoirs, and R5—Hăneşti reservoirs. In order to test the flood control capacity of the Bașeu multi-reservoir system, the Cal Alb (R1) dam break scenario (piping failure) was taken into account. Three 2D stream flow modeling configurations based on R1 inflow rate with a 1% (100 year), 0.5% (500 year), and 0.1% (1000 year) recurrence interval and the water volume which can be accumulated with that specific inflow rate (1% = 10.19 × 106 m3; 0.5% = 12.39 × 106 m3; 0.1% = 17.35 × 106 m3) were computed. The potential flood wave impact was achieved on the basis of different flood severity maps (e.g., flood extent, flood depth, flood velocity, flood hazard) generated for each recurrence interval scenario and highlighted within the built-up area of 27 settlements (S1–S27) located downstream of R1. The results showed that the multi-reservoir system of Bașeu River has an important role in flood mitigation and contributes to the APDF in the context of climate change and the intensification of hydrological hazard manifestation in northeastern Romania.

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.

Optimizing Current and Future Hydroelectric Energy Production and Water Uses of the Complex Multi-Reservoir System in the Aliakmon River, Greece

In this work we study long-term maximization of hydroelectric energy generation from complex multi-purpose reservoir systems, using the reservoir system of the Aliakmon River, Greece, as an application example. This system serves various purposes, like urban water supply, irrigation, hydroelectric energy production, cooling thermoelectric power plants and flood control, while preserving environmental flow. The system operator uses institutional rules for the annual scheduling of the outflows of the 2 largest reservoirs (Ilarion and Polyfyto) for additional safety and smooth distribution of energy production through the year. In this work, we focus on maximization of energy production. We have considered three different hydrological scenarios (dry, average and wet), both for the current and for anticipated future water demand. The multi-reservoir system’s operation was simulated and then optimized using a rather simple form of genetic algorithms, in order to maximize hydro energy production. All other water uses were taken into account as constraints. Our conceptual and computational approach succeeded to identify and quantify hydro energy production increase and to indicate necessary changes to the operating rule curves of the reservoirs. The methodology can be easily adapted to other large-scale multi reservoir systems.

Optimizing operating rules for multi-reservoir hydropower generation systems: An adaptive hybrid differential evolution algorithm

The optimization of hydropower multi-reservoir systems is one of the most challenging reservoir management problems due to the complex dynamic nonlinear influencing variables such as reservoir active storage volume, released water discharge rate, and water pressure head available for hydropower generation. To optimize such a complex problem in an efficient way, the development of a high-precision meta-heuristic algorithm are of essential significance. This paper develops an adaptive differential evolution with particle swarm optimization (A-DEPSO) algorithm to derive optimal operating rules for multi-reservoir systems with the purpose of hydropower production. A-DEPSO algorithm uses novel mutation and crossover mechanisms to improve global and local search capabilities, respectively. The application of the proposed method is verified to solve a complex four-reservoir hydropower generation system, located in the southwest of Iran. The results achieved indicate that the A-DEPSO reduced the value of the objective function by an average of 57% compared with other well-known optimizers in the literature. Furthermore, the total power generated from the four-reservoir system using the proposed method increased by an average of 11% compared to the other methods. Finally, the results obtained in this study confirm that the A-DEPSO can be used efficiently to solve complex multi-reservoir hydropower generation 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.

Retraction Note to: Application in optimization of multi-reservoir water systems using improving shuffled complex algorithm

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s42452-020-2590-x