Optimal Rule Curves Research Articles

Optimal Rule Curves for Operation the Euphrates River Concerning Tharthar Reservoir as a Case Study

Optimal Rule Curves for Operation the Euphrates River Concerning Tharthar Reservoir as a Case Study

Optimizing Solution in Decision Supporting System for River Basin Management Consisting of a Reservoir System

Decision support systems tackle problems and require systematic planning. They consider physical data, hydrological data, and sediment levels to achieve efficiency and adaptability in various situations. Therefore, this research aims to identify alternative engineering choices for the management of a river basin with a single reservoir system. Optimization techniques, including marine predator algorithm (MPA), genetic algorithm (GA), genetic programming (GP), tabu search (TS), and flower pollination algorithm (FPA), were applied to find the optimal reservoir rule curves using a reservoir simulation model. The study focused on the Ubolratana Reservoir in Thailand’s Khon Kaen Province, considering historic inflow data, water demand, hydrologic and physical data, and sedimentation volume. Four scenarios were considered: normal water scarcity, high water scarcity, normal excess water, and high excess water. The optimal rule curves derived from the reservoir simulation model, incorporating sedimentation and hedging rule (HR) criteria, were found to be the best engineering choices. In the normal and high water scarcity scenarios, they minimized the average water shortage to 95.558 MCM/year, with the lowest maximum water shortage 693.000 MCM/year. Similarly, in the normal and high excess water scenarios, the optimal rule curves minimized the average excess water, resulting in a minimum overflow of 1087.810 MCM/year and the lowest maximum overflow 4105.660 MCM/year. These findings highlight the effectiveness of integrating optimization techniques and a reservoir simulation model to obtain the optimal rule curves. By considering sedimentation and incorporating HR criteria, the selected engineering alternatives demonstrated their ability to minimize water shortage and excess water. This contributes to improved water resource management and decision-making in situations of scarcity and excess.

Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves

This paper presents a comprehensive review of optimization algorithms utilized in reservoir simulation-optimization models, specifically focusing on determining optimal rule curves. The study explores critical conditions essential for the optimization process, including inflow data, objective and smoothing functions, downstream water demand, initial reservoir characteristics, evaluation scenarios, and stop criteria. By examining these factors, the paper provides valuable insights into the effective application of optimization algorithms in reservoir operations. Furthermore, the paper discusses the application of popular optimization algorithms, namely the genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search (CS), and tabu search (TS), highlighting how researchers can utilize them in their studies. The findings of this review indicate that identifying optimal conditions and considering future scenarios contribute to the derivation of optimal rule curves for anticipated situations. The implementation of these curves can significantly enhance reservoir management practices and facilitate the resolution of water resource challenges, such as floods and droughts.

Application of Optimization Techniques for Searching Optimal Reservoir Rule Curves: A Review

This paper reviews applications of optimization techniques connected with reservoir simulation models to search for optimal rule curves. The literature reporting the search for suitable reservoir rule curves is discussed and examined. The development of optimization techniques for searching processes are investigated by focusing on fitness function and constraints. There are five groups of optimization algorithms that have been applied to find the optimal reservoir rule curves: the trial and error technique with the reservoir simulation model, dynamic programing, heuristic algorithm, swarm algorithm, and evolutionary algorithm. The application of an optimization algorithm with the considered reservoirs is presented by focusing on its efficiency to alleviate downstream flood reduction and drought mitigation, which can be explored by researchers in wider studies. Finally, the appropriate future rule curves that are useful for future conditions are presented by focusing on climate and land use changes as well as the participation of stakeholders. In conclusion, this paper presents the suitable conditions for applying optimization techniques to search for optimal reservoir rule curves to be effectively applied in future reservoir operations.

Design of optimal operating rule curves for hydropower multi-reservoir systems by an influential optimization method

Hydropower plants play an integral role in energy supply and are one of the main renewable energy sources. The efficiency of hydropower reservoir systems is controlled by operating rules that need to be optimized to maximize energy production. Here, a novel optimization algorithm, influential flower pollination algorithm (IFPA), is developed in this paper. IFPA has three enhancements: an efficient exploration mechanism based on an adaptive coefficient, a new exploitation mechanism around the best solution, and a rank-based strategy to smoothly transition from exploration to exploitation. IFPA is tested in two benchmark multi-reservoir systems and a real-world five-reservoir hydropower system in Iran. The results show IFPA outperforms other advanced methods in terms of convergence speed and preciseness to achieve the global solution. The tested algorithms include adaptive guided differential evolution (AGDE), composite DE (CODE), hybridizing sum-local search optimizer (HSLSO), improved teaching learning-based optimization (ITLBO), self-adapting control parameters in DE (jDE), self-adaptive TLBO (SATLBO), and a multi-strategy hybrid of DE and particle swarm optimization (MS-DEPSO). IFPA also achieved better total power production in the real-world case study compared to other algorithms. Overall, IFPA can provide better solutions with less power deficit and shows as a promising method for optimizing hydropower reservoir problems.

Optimal Choices in Decision Supporting System for Network Reservoir Operation

The aim of this research was to identify optimal choices in decision support systems for network reservoirs by using optimal rule curves under four scenarios related to water scarcity and overflow situations. These scenarios were normal water shortage, high water shortage, normal overflow and high overflow situations. The application of various optimization techniques, including Harris Hawks Optimization (HHO), Genetic Algorithm (GA), Wind-Driven Optimization (WDO) and the Marine Predator Algorithm (MPA), in conjunction with a reservoir simulation model, was conducted to produce alternative choices, leading to suitable decision-making options. The Bhumibol and Sirikit reservoirs, situated in Thailand, were selected as the case study for the network reservoir system. The objective functions for the search procedure were the minimal average water shortage per year, the minimal maximum water shortage and the minimal average water spill per year in relation to the main purpose of the reservoir system using the release criteria of the standard operating policy (SOP) and the hedging rule (HR). The best options of each scenario were chosen from 152 options of feasible solutions. The obtained results from the assessment of the effectiveness of alternative choices showed that the best option for normal water scarcity was the rule curve with the objective function of minimal average water shortage per year, using HR and recommended SOP for operation, whereas the best option for high-water shortage situation was the rule curves with objective function of minimal of maximum water shortage using HR and recommended HR for operation. For overflow situation, the best option for normal overflow situation was the rule curves with objective function of minimal average water spill per year using HR and the recommended SOP for operation, whereas the best option for the high overflow situation was the rule curve with the objective function of minimal average water spill per year using HR and the recommended HR for operation. When using the best curves according to the situation, this would result in a minimum water shortage of 153.789 MCM/year, the lowest maximum water shortage of 1338.00 MCM/year, minimum overflow of 978.404 MCM/year and the lowest maximum overflow of 7214.00 MCM/year. Finally, the obtained findings from this study would offer reliability and resiliency information for decision making in reservoir operation for the multi-reservoir system in the upper region of Thailand.

Extracting Optimal Operation Rule Curves of Multi-Reservoir System Using Atom Search Optimization, Genetic Programming and Wind Driven Optimization

This research aims to apply optimization techniques using atom search optimization (ASO), genetic programming (GP), and wind-driven optimization (WDO) with a reservoir simulation model for searching optimal rule curves of a multi-reservoir system, using the objective function with the minimum average quantity of release excess water. The multi-reservoir system consisted of five reservoirs managed by a single reservoir that caused severe problems in Sakon Nakhon province, Thailand, which was hit by floods in 2017. These included Huai Nam Bo Reservoir, the Upper Huai Sai-1 Reservoir, the Upper Huai Sai-2 Reservoir, the Upper Huai Sai-3 Reservoir, and the Huai Sai Khamin Reservoir. In this study, the monthly reservoir rule curves, the average monthly inflow to the reservoirs during 2005–2020, the water demand of the reservoirs, hydrological data, and physical data of the reservoirs were considered. In addition, the performance of the newly obtained rule curves was evaluated by comparing the operation with a single reservoir and the operation with a multi-reservoir network. The results showed situations of water shortage and water in terms of frequency, duration, average water, and maximum water. The newly obtained rule curves from the multi-reservoir system case showed an average water excess of 43.722 MCM/year, which was less than the optimal curves from the single reservoir case, where the average water excess was 45.562 MCM/year. An analysis of the downstream reservoir of the multi-reservoir system, which diverts water from the upstream reservoirs, was performed. The results showed that the new optimal rule curves of ASO, GP, and WDO operated as a multi-reservoir system performed better than when operated as a single reservoir. Therefore, this research is suitable for sustainable water management without construction.

Application of Harris Hawks Optimization with Reservoir Simulation Model Considering Hedging Rule for Network Reservoir System

This research aims to apply the Harris hawks optimization (HHO) technique connected with a reservoir simulation model to search optimal rule curves of the network reservoir system in Thailand. The downstream water demand from the network reservoir that required shared water discharge, hydrological data, and physical data were considered in the reservoir simulation model. A comparison of the situation of water shortage using optimal rule curves from HHO technique, genetic algorithm (GA), and wind-driven optimization (WDO) is presented. The results showed that the new rule curves derived from the HHO technique with network reservoir searching were able to alleviate the water shortage and over-flow situations better than the current rule curves. The efficiency of using rule curves from HHO technique compared to GA and WDO techniques showed that the HHO technique can provide a better solution that reduced water scarcity and average over-flow compared with the current rule curves by up to 4.80%, 4.70%, and 4.50%, respectively. In addition, HHO was efficient in converging rule curve solutions faster than GA and WDO techniques by 15.00% and 54.00%, respectively. In conclusion, the HHO technique can be used to search for optimal network reservoir rule curves solutions effectively.

A trade-off analysis of adaptive and non-adaptive future optimized rule curves based on simulation algorithm and hedging rules

A trade-off analysis of adaptive and non-adaptive future optimized rule curves based on simulation algorithm and hedging rules

Applying of Marine Predators Algorithm Linked with Reservoir Simulation Model considering Sedimentation for Reservoir Operation

This paper proposes the Marine Predators algorithm (MPA) linked with the reservoir simulation model and considering sedimentation, in order to improve reservoir rule curves. The release criteria of the hedging rule (HR) and standard operating policy (SOP) were investigated in this study. The results showed that the patterns of the new optimal rule curves from the MPA technique considering sedimentation and using HR were practically useful in this study, as were the patterns of the new optimal rule curves using SOP and those of the existing rule curves. Furthermore, the new optimal rule curves using HR criteria were able to alleviate both water scarcity and excess water situations better than both existing rule curves and optimal rule curves using SOP in terms of minimal average water shortage. The new curves reduced minimal average water shortage by 53% and excess release water deficit by 19%, whereas the frequency of water shortage term was increased by 3%. The results of rule curves efficiency from MPA were higher than GA and FPA techniques in terms of providing solutions. There was a significant difference in the efficiency of water problem alleviation between considering and not considering sedimentation. It can be concluded that the MPA linked with reservoir simulation using HR criteria and considering sedimentation can be used to find optimal rule curve solutions effectively.

Optimization of the Operation Rule Curves for Cascade Reservoirs Using the Cuckoo Search Algorithm

Reservoir operation optimization models are based on the hydrological cycle principle. These models determine the inflow and outflow necessary to meet the demands for drinking water, irrigation water, etc. Rather than optimizing each reservoir separately in cascaded reservoir systems, more efficient results are obtained if the reservoirs are optimized as a whole. In this study, the optimal operation rule curves for cascaded reservoirs were obtained by using the cuckoo search algorithm, which is a soft computing method. Both the irrigation and flood control constraints of the Adiguzel and Cindere dams in Denizli (Turkey) were satisfied by utilizing these rule curves, and the total energy production was maximized. In addition, these rule curves considered turbine efficiency, which significantly contributes to published literature. The total energy obtained with the proposed operation rule curves was 14% higher than that currently produced by the Adiguzel and Cindere dams.

Optimization of hydropower and related benefits through Cascade Reservoirs for sustainable economic growth

Reservoirs are major indicators of sustainable socio-economic development, agriculture and energy. This study focused on genetic algorithm based optimization of rule curves for maximizing the hydropower and associated benefits through cascade reservoirs. Optimization has been carried out using RESOOSE model for maximizing hydro-power, irrigation and evacuation of sediment from multipurpose Diamer Basha and Terbela Reservoirs on Indus River in Pakistan. The best methods and values of Genetic Algorithm computed through sensitivity analysis were tournament selection method with 2 teams, population size 288, single point crossover method with crossover probability 0.8 and adapt feasible mutation method. The policy equations on storage, outflows and hydropower for the cascade reservoirs in series were formulated. The maximum annual overall benefits were 3380 Million US$ for multi-objective optimization of joint operation of Terbela and Diamer Basha Reservoirs, where hydropower was major contributor among all the associated benefits with highest annual benefits of 2724.7 Million US$. The results demonstrate that hydropower has been optimized along with maximization of the associated benefits. The optimized rule curves of the cascade reservoirs developed in the study have enhanced the overall benefits for achieving SDGs of zero hunger, affordable clean energy, economic growth and water availability.

Development of Decision-Making Support Tools for Future Reservoir Management Under Climate and Land Cover Variability: a Case Study

The effects of climate change have been already experienced globally. The uncertainties of climate and land cover changes in the watershed above the reservoir have direct impacts on the inflow into the reservoir and the water resource management that uses reservoir rule curves. This study aims to assess the impacts of climate and land cover changes on the inflow into the Huai Luang Reservoir, Udonthani province during the period of 2018-2067 by using the simulation of two climate scenarios with the data from the PRECIS model under the emission conditions of B2. The prediction of future inflow has used the InVEST model. The improvement of the reservoir rule curves has used the Cultural Algorithm (CA) technique connected with the reservoir system simulation model. Monthly inflow data flowing into the reservoir from the past 33 years has been used to synthesize 1,000 events in order to evaluate the efficiency of the new obtained reservoir rule curves. Therefore, the data results of water shortage and excess water discharge situation have been illustrated in form of frequency, magnitude, duration of events, average amount of water, and maximum amount of water. The results have showed that average rainfall and temperature have tended to increase. The optimal reservoir rule curves improved by the CA technique can effectively reduce the frequency of water shortage situations and the one of the excess water discharge occurring during inflow changes in normal situation, and future situation under emission scenario B2.

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.

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.

Optimization of reservoir operating curves and hedging rules using genetic algorithm with a new objective function and smoothing constraint: application to a multipurpose dam in Morocco.

Long-term operation optimization of multipurpose reservoirs is highly important in arid and semi-arid countries challenged by climate change. This paper suggests an objective function combining two competitive shortage indicators for multi-objective reservoir operation optimization. An improved genetic algorithm including a smoothing constraint, reducing infeasible fluctuations of the operation policy, is developed to solve this problem. Operating curves were optimized jointly to hedging factors aiming at avoiding severe droughts and high damages for users. The proposed function was compared with the conventional objective function of minimizing the sum of squared deviations (SSD) between releases and demands. Different combinations of weights of the objectives linked to the Moroccan reservoir were studied. The proposed objective function yields to improved results in terms of computation requirements since it converges quicker and it leads to better supply performance. For drinking water use, the frequency of shortage was reduced by 66% and the maximum deficit by 14% whereas for irrigation the frequency of shortage was curtailed by 6%. The operating curves obtained by the developed optimization model were then compared with static operating rule curves simulated in RIBASIM. The superiority of variable optimized rule curves was proven compared with stable operating mode over time.

Optimal Multi-Reservoir Operation for Hydropower Production in the Nam Ngum River Basin

This research aims to investigate optimal hydropower production of multi-reservoirs in Lao PDR and develop optimal reservoir rule curves. The Nam Ngum 1 and 2 (NN1 and NN2, respectively) reservoirs in the Nam Ngum River basin (NNRB), which is located in the middle of Laos, are selected as study areas. Mixed integer nonlinear programming (MINLP) is developed as an optimization model to maximize the hydropower production of joint reservoir operation of NN1 and NN2. The optimal operation rule curves are established by using the storage level estimated by the optimization model. Given the limited sideflow data, an integrated flood analysis system (IFAS) and water balance equation are used to simulate the sideflow into NN1 reservoir. A good fit is observed between the monthly streamflow simulated by IFAS and that calculated by the water balance equation. Compared with the observed data, the MINLP model can increase the annual and monthly hydropower production by 20.22% (6.01% and 14.21% for NN1 and NN2, respectively). The water storage level estimated by the MINLP model is used to build the operation rule curves. Results show that the MINLP model of multi-reservoir is a useful and effective approach for multi-reservoir operations and is expected to hold high application value for similar reservoirs in NNRB.

An integrated simulation-optimization framework to optimize the reservoir operation adapted to climate change scenarios

In order to optimize the operation rule curve of the Shahrchay reservoir in the north-west of Iran under climate change, a new integrated simulation-optimization framework under different climate change scenarios was introduced in the current study. The proposed framework contains several steps: First, the more accurate general circulation models (GCMs) were chosen among several available GCMs and the predictors screening based on decision tree model (M5) was applied to select the most important predictors among enormous number of potential large-scale climate variables of GCMs. Then, the artificial neural networks (ANNs) were trained by the observed temperature and precipitation time series and the selected dominant predictors to downscale the precipitation and temperature parameters of GCMs for the base period (1951–2000) over the case area and the future monthly predictions of temperature and precipitation were implemented by the trained ANNs under A1B, B1, RCP4.5, and RCP8.5 climate change scenarios to assess the future changes of precipitation and temperature between 2020 and 2060. Thereafter, the estimated precipitation values were imposed to the hybrid Wavelet-M5 model to simulate inflow (runoff) to the Shahrchay reservoir and finally, genetic algorithm (GA) was used in order to optimize the operation rule curves of the reservoir system using the predicted average monthly precipitation, evaporation, inflow and considering water supply for agricultural and municipal targets and minimization of total squared deficiencies for future. The Wavelet-M5 model could result in a reliable performance in the one-step-ahead runoff prediction as the obtained correlation coefficient (CC) for training and verifying data sets were 98% and 97.7%, respectively. The results showed that the average long-term annual runoff volume may be decreased between 0.08% and 2.27% in the future with regard to the base period, and the simulation results for present and future conditions indicated a decrease in water availability. Also, the estimated optimal rule curves for the reservoir showed a different shape for all scenarios employed in this study.

Reducing lake water-level decline by optimizing reservoir operating rule curves: A case study of the Three Gorges Reservoir and the Dongting Lake

Water levels of a lake are critical for functions such as navigation, water supply and ecological services; however, the lake water level can be reduced significantly by upstream reservoir refill operations. In this study, the reservoir operating rule curves were redesigned to satisfy the lake’s water demand. Variations in lake water level in response to reservoir releases upstream are simulated by using a river-lake model, which is simple and less time-consuming than hydrodynamic models. In the river-lake model, inflow to the lake and outflow from the lake are derived by using a series of regression models of upstream reservoir releases. Then optimization operation model is developed to minimize the lake water-level decline with the restored water level as the benchmark, and to maximize the benefits of hydropower generation simultaneously. The NSGA-II algorithm is used to solve the optimization model, where a moving-average filter is embedded to smooth the operating rule curves. China’s Dongting Lake and its upstream reservoir, the Three Gorges Reservoir were selected as a case study. The proposed river–lake model is effective for simulating the lake water level variations, with a Nash–Sutcliffe Efficiency coefficient of 0.94. With the derived optimal operating rule curves, the lake water-level decline caused by conventional reservoir operation during the refill period is reduced by 5.0%, and the hydropower generation and hydropower reliability are improved by 3.9% and 8.3%, respectively. Therefore, the proposed method is an effective tradeoff between lake restoration and enhancing the benefits of hydropower.

Hydropower reservoir reoperation to adapt to large-scale photovoltaic power generation

Integrating dispatchable hydropower with nondispatchable photovoltaic (PV) power is a promising way to enhance resource use efficiency. However, hybrid generation of these energy sources may exert greater pressure on the integrated water resources management, calling for reservoir reoperation. To address this issue, we propose a procedure to derive adaptive operating rules for a large hydro—PV hybrid power plant consisting of following steps: (1) establish a short-term simulation model to estimate the PV curtailment rate arising from specified long-term hydropower output, in which the relationships are represented as PV energy-loss functions to bridge long- and short-term operations; (2) design six operating rules that incorporate the PV energy-loss functions to simulate the system's long-term operation; and (3) develop a multi-objective optimization model to identify the most effective operating rules. A case study was carried out for China's Longyangxia hydro—PV hybrid power plant. Results showed that, compared with traditional operation, the average annual energy production and power supply reliability of the optimal rule curves increased to 7.3 billion kWh (4.3%) and 90% (47.5%), respectively, while the water shortage index decreased to 114 (6.6%). The derived operating rules could achieve good balance between PV integration and water management.