Multipurpose Reservoir Operation Research Articles

Unwrapping the triptych of climatic, social and energy-market uncertainties in the operation of multipurpose hydropower reservoirs

The ongoing debate about hydropower and its role in the transition towards renewable energy development is strongly associated with its sustainable character, social and environmental footprint, and potential benefits. Multipurpose hydropower reservoirs play a significant dual role as they serve both water and energy uses. However, their operation and management are subject to inherently uncertain processes, future challenges, and externalities originating from climate, society and the energy market. In this context, we contribute an uncertainty-aware optimization methodology that supports operators in accounting for the cascade effects of three main uncertain drivers, i.e., rainfall, water demands, and energy scheduling. To describe climatic and energy-market uncertainties, stochastic approaches are followed to generate 2000 years of daily synthetic rainfall and electricity price data, respectively. To account for human-oriented procedures, i.e., water and energy targets, statistical analyses of historical abstractions are employed to fit copula-based relationships, in which the desirable releases for energy production depend on day-ahead electricity prices. Finally, a toolbox is established, offering insights for decision-making regarding the estimated profits, their expected changes, and the associated risk due to climate or market-oriented shifts. Our approach is demonstrated in a multipurpose reservoir in Greece, Plastiras, which is affected by rapidly increasing socioeconomic conflicts. Our findings indicate that an improved understanding of uncertainty can lead to more efficient operation policies and shield operator from misleading perceptions and their often-detrimental effects. Specifically, in the high electricity price era the expected profits are substantially unstable, since a suboptimal trade-off between irrigation and energy production may be risky and costly.

The development of inflow discharge for the karian multipurpose reservoir in the pursuit of reservoir management optimization

Climate change and land cover modifications result in alterations in hydrological regimes. The demand for water and electrical energy continually rises along with population growth. Consequently, optimizing the Karian Multipurpose Reservoir (Hydroelectric Exploitation) operation requires considering hydrological regime changes. The Karian multipurpose reservoir is designed to meet water needs in Banten Province. The strategic policy for the Reservoir is to provide raw water at a rate of 14.6 m3/s. Of this, 9.1 m3/s is diverted to Tangerang Regency, and 5.5 m3/s is directed to Serang City through Ciujung River. Before being released into the Ciujung River, a flow of 5.5 m3/s is channeled through turbines to generate 1.8 MW of electricity. This study compares two models for estimating discharge to support Karian Reservoir management: (1) The Rainfall Conversion Ratio (CR) and Catchment Area, and (2) The Continuous model. The Rainfall CR and Catchment Area has a correlation value of 0.523 with CR KOICA, Absolute Relative Error (ARE) of 0.54, and Root Mean Square Error (RMSE) of 10.61. Meanwhile, the continuous model has a correlation value of 0.877 with CR KOICA, ARE of 0.18, and RMSE of 3.64. The results indicate the continuous model is the most effective in estimating input discharge.

Multi-purpose reservoir operation concomitant with estimating hydropower potential using multifarious hydrological models

The research aims at determining the optimal release rule to increase the capacity of Rib reservoir. The reservoir inflow using HBV-light hydrological model embracing optimal reservoir operation through HEC-ResSim model were used to prepare an optimum operational plan. The potential of the river for hydropower generation prioritise the demand at a specified level regarding storage capacity (m3), level of reservoir (m), and the relation between inflow and outflow of the reservoir. From the model performance features, the coefficient of correlation (R2) and Nash Sutcliffe Efficiency (NSE) were determined to be, respectively, 0.77 and 0.73 for calibration and 0.72 and 0.70 for validation. The Sobol approach was used for detailed sensitivity analysis of DROP model parameters based on the performance of C2M on outflows and volumes. The results suggest that the threshold coefficient characterizing the demand-controlled release level is the most significant parameter. According to the simulation's findings, the reservoir's average regulated release is calculated to be 22.86 m3/s, and its average monthly hydropower output is 6.73 MW. Average annual hydropower energy was estimated as 58.955 GW h/year and mean annual inflow of reservoir volume of water to be 223.54 Mm3. This volume of water is adequate to accommodate total annual irrigation demand, environmental obligation, and other respective requirements in the downstream. The demand for hydropower and irrigation and supply from reservoir capacity can be counterbalanced from the simulated result without any hindrance.

Derivation of Optimal Two Dimensional Rule Curve for Dualistic Reservoir Water-Supply System

In arid and semi-arid regions particularly vulnerable to climate change, optimizing the long-term operation of multi-purpose reservoirs is paramount. This study derived an optimum two-dimensional rule curve to jointly operate the parallel reservoirs of Mosul and Dukan, Northern Iraq. A hybridized optimization technique combining conventional dynamic programming with the shuffled complex evolution algorithm (SCE-UA) was developed to solve this problem. The results showed that the proportion of normal water supply areas increased from the beginning of the flood season (October) to its highest levels in April (58.77% of the total water supply area). The proportion decreased to its lowest in September (25.04% of the total water supply area). The newly derived 2D rule cure was compared with the current operation policy and was found to optimize the amount of water shortage by 21.1% during the operational period. It also reduced the shortage period and avoided catastrophic water shortages during droughts. In addition, the developed model optimized the amounts of water more than the joint water requirements, suffering from a significant deficit in meeting the demand during some months of the operational years. As a result, the storage in each reservoir was improved and thence can be adapted to face water shortages during future climate changes. This study proved the new hybridized model's applicability and can serve as a tool for sustainable water management. Doi: 10.28991/CEJ-2023-09-07-016 Full Text: PDF

Development of water re-allocation policy under uncertainty conditions in the inflow to reservoir and demands parameters: a case study of Karaj AmirKabir dam

The process of optimal operation of multipurpose reservoirs is accompanied by large dimensions of decision variables and the uncertainty of hydrological parameters and water demands. Therefore, in determining the optimal operation policies (OOPs), the decision making for water allocation is faced with problems and complexities. One of the effective approaches for sustainable management and optimal allocation from water resources is the multi-objective structural development based on the uncertainty of input parameters. The purpose of this study is to provide OOPs from Karaj AmirKabir multi-purpose reservoir with applying uncertainty in the inflow to reservoir and downstream water demand. The proposed approach has been investigated in two certain and uncertain models, and three objective functions of the system including maximizing hydropower generation, water supply demands, and flood control have been considered to formulate OOPs. Non-dominated sorting genetic algorithm-II (NSGA-II) was performed to optimize the three proposed objective functions and by applying multi-criteria decision-making (MCDM) methods, the best operation scenario was selected. In the uncertainty model, using the interval method and repeated implementation of the deterministic model for completely random scenarios that generated based on the variation interval of the uncertain parameters, the non-deterministic optimal allocation values were produced. Based on these optimal allocation values and the fitting of the standard probability distribution on it, the probability of occurrence of the deterministic allocation values was determined. Production of optimal probabilistic allocation policies can be very useful and efficient in providing real vision to managers to select appropriate policies in different conditions and rare hydrological events. The results obtained from the certain model shows that as a result of optimal allocation to demands, the fuzzy reliability, resiliency, and system stability indexes were improved to 67.81, 21.99, and 24.98 percentage, respectively. Also, in an uncertain model, applying changes of 48% and 22%, respectively, for the inflow and downstream demand has led to changes of 23%, 55%, and 18%, respectively, in the first, second, and third objective functions. The highest impact from uncertain conditions, has been related to the water supply demands with 55% of the range of variations So, the water supply demands, has a higher sensitivity and priority than other reservoir objective functions under uncertain conditions. Another important result extracted from this study is to determine the monthly probability of optimal allocations achievement. Accordingly, in the warm seasons and years in which the reservoir is facing drought, the occurrence probability of the optimal allocations decreases. Given the comprehensiveness of the proposed methodology, this approach is a very suitable tool for determining the optimal water allocations as probabilistic based on the scenarios desired by managers and reservoir operators.

Dynamic Rule Curves and Streamflow under Climate Change for Multipurpose Reservoir Operation Using Honey-Bee Mating Optimization

Climate change in the watershed above the reservoir has a direct impact on the quantity of streamflow that enters the reservoir and the management of water resources. Developing effective reservoir rule curves helps reduce the risk of future failures of water resource management. The purpose of this study was to analyze the influence of climate change on the volume of streamflow entering the Ubolratana Reservoir, Thailand during the years 2020–2049 with climate simulations from the CIMP5 model under RCP4.5 and RCP8.5 scenarios. SWAT models were used to forecast future reservoir streamflow quantities. Moreover, suitable reservoir rule curves using the Honey-Bee Mating Optimization (HBMO) were developed and the effectiveness of the new rule curves was assessed. According to the research findings, the average yearly streamflow in the future apparently grew from 32% in the base years (2011–2019) and 65% under the RCP4.5 and RCP8.5 scenarios, respectively. It was discovered that the average monthly streamflow was higher in the rainy season than in the dry season. Both of the projected situations have a form compatible with the present rule curves in the section of the new reservoir rule curves generated with the HBMO. Furthermore, the newly constructed rule curves may allow the reservoir to keep more water during the rainy season, thereby assuring that there will be adequate water during the following dry season. Additionally, during the dry season, the reservoir was able to release more water that would be able to reduce the water shortage, indicating that it was able to effectively reduce the amount of water shortage and average overflow under RCP4.5 and RCP8.5 situations.

Reducing impacts of rice fields nitrate contamination on the river ecosystem by a coupled SWAT reservoir operation optimization model

The present study proposes a multipurpose reservoir operation optimization for mitigating impact of rice fields’ contamination on the downstream river ecosystem. The developed model was applied in the Tajan River basin in Mazandaran Province, Iran, in which the rice is the main crop. We used soil and water assessment tool (SWAT) to simulate inflow of the reservoir and nitrate load at downstream river reach. Nash–Sutcliffe model efficiency coefficient was used to measure the robustness of SWAT. NSE indicated that SWAT is acceptable to simulate nitrate load of the rice fields. The results of SWAT was applied in the structure of a multipurpose reservoir operation optimization in which three metaheuristic algorithms including differential evolution algorithm, particle swarm optimization and biogeography-based algorithm were utilized in the optimization process. Reliability index, mean absolute error and failure index were used to measure the robustness of the optimization algorithms. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution was utilized to select the best algorithm. Based on results, particle swarm optimization is the best method to optimize reservoir operation in the case study. The reliability index and mean absolute error for water supply are 0.6 and 5 million cubic meters, respectively. Furthermore, the failure index of contamination is 0.027. Hence, it could be concluded that the proposed optimization system is reliable and robust to mitigate losses and nitrate contamination simultaneously. However, its performance is not perfect for minimizing impact of contamination in all the simulated months.

Multi-objective reservoir operation of the Ukai reservoir system using an improved Jaya algorithm

Abstract This paper introduces an effective and reliable approach based on a multi-population approach, namely the self-adaptive multi-population Jaya algorithm (SAMP-JA), to extract multi-purpose reservoir operation policies. The current research focused on two goals: minimizing irrigation deficits and maximizing hydropower generation. Three different models were formulated. The results were compared with those for an ordinary Jaya algorithm (JA), particle swarm optimization (PSO), and an invasive weed optimization (IWO) algorithm. In Model-1, the minimum irrigation deficit obtained by SAMP-JA and JA was 305092.99 . SAMP-JA was better than JA, PSO and IWO in terms of convergence. In Model-2, the maximum hydropower generation achieved by SAMP-JA, JA and PSO was 1723.50 . When comparing the average hydropower generation, SAMP-JA and PSO performed better than JA and IWO. In terms of convergence, SAMP-JA was better than PSO. In Model-3, a self-adaptive multi-population multi-objective Jaya algorithm (SAMP-MOJA) was better than multi-objective particle swarm optimization (MOPSO) and multi-objective Jaya algorithm (MOJA) in terms of maximum hydropower generation, and MOPSO was better than SAMP-MOJA and MOJA in terms of minimum irrigation deficiency. While comparing convergence, SAMP-MOJA was found to be better than MOPSO and MOJA. Overall, SAMP-JA was found to outperform JA, POS and IWO.

Multi-objective game theory optimization for balancing economic, social and ecological benefits in the Three Gorges Reservoir operation

Reservoir operation is an important and effective measure for realizing optimal allocation of water resources. It can effectively alleviate regional scarcity of water resources, flood disasters and other social problems, and plays an important role in supporting sustainable strategic development of water resources. Coordinating the stakeholders is key to the smooth operation of a multifunctional reservoir. This research examines the competition among stakeholders of a multi-objective ecological reservoir operation aiming to provide for economic, social and ecological demands. A multi-objective game theory model (MOGM) specified 10-day water discharge to meet the triple water demands (power generation, socio-economic consumption and environment) for multi-purpose reservoir operation. The optimal operation of the Three Gorges Reservoir (TGR), with the ecological objective of providing comprehensive ecological flow demanded for some key ecological problems that may occur in the middle and lower reaches of the Yangtze River, was chosen as a case study. Discharged water calculated by the MOGM and a conventional multi-objective evolutionary algorithm/decomposition with a differential evolution operator was then allocated to different demands. The results illustrate the applicability and efficiency of the MOGM in balancing transboundary water conflicts in multi-objective reservoir operation that can provide guidance for the operation of the TGR.

Smart supply-side management of optimal hydro reservoirs using the water/energy nexus concept: A hydropower pinch analysis

A novel, smart, supply-side management approach is proposed for optimal operation of multi-purpose hydro reservoirs using the water/energy nexus concept and introducing a hydropower pinch analysis (HyPoPA). The nexus among water and energy sources and sinks are considered to develop hydropower composite curves, grand composite curves, and continuous composite curves. These graphical tools are accompanied by hydropower cascade tables to facilitate numerical analysis of conservation and recovery of water resources at high resolution. The minimum hydro storage is targeted, and three management cases are determined by obtaining the surplus (or deficit) head of a hydro reservoir in successive operational years in unreliable, reliable, and self-sufficient cases. The effects of climate change are predicted using smart algorithms to manage varying downstream energy and water sinks under optimal conditions by HyPoPA. Two prediction scenarios are developed to mimic annual operation and online monitoring cases using advanced neural networks. Karkheh hydro reservoir serves as a case study to verify smart HyPoPA. The results showed that the sources were successfully predicted employing a hybrid long short-term memory and gated recurrent unit network in 2018 (R2 = 97.3%, MAPE = 15.9%), which was a dry year when reservoir water levels fell in a non-reliable case with deficit head. The Karkheh reservoir produced 37.2 GWh more hydroelectricity and saved 1.66 billion m3 of water after meeting all water requirements using the smart HyPoPA in the target year.

Multipurpose Reservoir Operation: a Multi-Scale Tradeoff Analysis between Hydropower Generation and Irrigated Agriculture

Reservoir operations often require balancing among several water uses. Despite the non-consumptive nature of hydropower, conflicts exist between irrigation and hydropower due to a demand seasonality mismatch. Hydropower operations are scheduled as part of a large-scale power grid, whereas irrigation decisions takes place at a smaller scale, most often the river basin. Balancing these water uses should involve a co-optimization at the power grid level, integrating all basins contributing hydropower to the grid. However, grid-wide co-optimization is not always possible due, for instance, to separate regulatory settings between water uses. For those cases, we propose a basin-wide co-optimization approach that integrates two decision scales—power grid and river basin— into a hydro-economic model. Water for irrigation is usually allocated by water rights or binding contracts, represented as constraints on grid-wide power operation models. We propose a water allocation scheme that integrates monthly marginal benefits of water for irrigation and hydropower at the basin level. Monthly water demand functions for irrigation are developed using an agricultural economic model, and marginal benefits of hydropower production are derived from a cost-minimization, grid-wide power scheduling model. Results for 50 inflow scenarios show that the proposed basin-wide co-optimization provides an economically sound operation. Total benefits from water use in the basin are on average 2.5% higher than those obtained under mandatory irrigation. Moreover, expected benefits under co-optimization are 5.4% and 1.8% higher for irrigated agriculture and hydropower, respectively, alleviating the conflicts between water uses in the basin.

Improving Daily Peak Flow Forecasts Using Hybrid Fourier-Series Autoregressive Integrated Moving Average and Recurrent Artificial Neural Network Models

In multi-purpose reservoirs, to achieve optimal operation, sophisticated models are required to forecast reservoir inflow in both short- and long-horizon times with an acceptable accuracy, particularly for peak flows. In this study, an auto-regressive hybrid model is proposed for long-horizon forecasting of daily reservoir inflow. The model is examined for a one-year horizon forecasting of high-oscillated daily flow time series. First, a Fourier-Series Filtered Autoregressive Integrated Moving Average (FSF-ARIMA) model is applied to forecast linear behavior of daily flow time series. Second, a Recurrent Artificial Neural Network (RANN) model is utilized to forecast FSF-ARIMA model’s residuals. The hybrid model follows the detail of observed flow time variation and forecasted peak flow more accurately than previous models. The proposed model enhances the ability to forecast reservoir inflow, especially in peak flows, compared to previous linear and nonlinear auto-regressive models. The hybrid model has a potential to decrease maximum and average forecasting error by 81% and 80%, respectively. The results of this investigation are useful for stakeholders and water resources managers to schedule optimum operation of multi-purpose reservoirs in controlling floods and generating hydropower.

Spatial and sectoral benefit distribution in water-energy system design

The design of water and energy systems has traditionally been done independently or considering simplified interdependencies between the two systems. This potentially misses valuable synergies between them and does not consider in detail the distribution of benefits between different sectors or regions. This paper presents a framework to couple integrated water-power network simulators with multi-objective optimisation under uncertainty to explore the implications of explicitly including spatial topology and interdependencies in the design of multi-sector integrated systems. A synthetic case study that incorporates sectoral dependencies in resource allocation, operation of multi-purpose reservoirs and spatially distributed infrastructure selection in both systems is used. The importance of explicitly modelling the distribution of benefits across different sectors and regions is explored by comparing different spatially aggregated and disaggregated multi-objective optimisation formulations. The results show the disaggregated formulation identifies a diverse set of non-dominated portfolios that enables addressing the spatial and sectoral distribution of benefits, whilst the aggregated formulations arbitrarily induce unintended biases. The proposed disaggregated approach allows for detailed spatial design of interlinked water and energy systems considering their complex regional and sectoral trade-offs. The framework is intended to assist planners in real resource systems where diverse stakeholder groups are mindful of receiving their fair share of development benefits.

A system dynamics simulation approach for environmentally friendly operation of a reservoir system

Multipurpose reservoir operation is a very complex decision-making process because of its multiple and usually conflicting targets. A system view of the interactions between various reservoir purposes (like for example, hydropower production and environmental impacts such as fish habitats, sediment flushing, and landslide stability) is a prerequisite to attain the full benefits from reservoir operations. System dynamics simulation is a well suited approach for dealing with complex time-varying systems and is widely used in water resources management and analyses of global change. This paper presents the development of a generic system dynamics simulation approach (SDSA) for the analyses of interactions between reservoir’s various functions. The implementation of SDSA includes two steps. First, the causal diagram of the reservoir system is used to map and analyze the main interactions among reservoir system’s different components, identify driving feedback relationships and use the system structure to obtain insights’ into system behavior. In the second step, the stock and flow diagraming is used to quantify those feedbacks and interactions by introducing a set of physical and empirical relationships required to simulate the model. The developed SDSA is applied to the Three Gorges Reservoir in China. Scenario based simulation analyses are performed with the SDSA model to increase the understanding of placing different priorities on the interactions between hydropower production and environmental impacts of reservoir operations. Results from model validation and sensitivity analyses suggest that the developed SDSA model is capable of simulating interactions among reservoir’s various functions, and the case study application demonstrates the utility of the model for capturing the main characteristics of reservoir system dynamic behavior. The insights gained from model simulations can assist in environmentally friendly operations of the reservoir.

Flood Control Versus Water Conservation in Reservoirs: A New Policy to Allocate Available Storage

The aim of this study is to contribute to solving conflicts that arise in the operation of multipurpose reservoirs when determining maximum conservation levels (MCLs). The specification of MCLs in reservoirs that are operated for water supply and flood control may imply a reduction in the volume of water supplied with a pre-defined reliability in the system. The procedure presented in this study consists of the joint optimization of the reservoir yield with a specific reliability subject to constraints imposed by hydrological dam safety and downstream river safety. We analyzed two different scenarios by considering constant or variable initial reservoir level prior to extreme flood events. In order to achieve the global optimum configuration of MCLs for each season, we propose the joint optimization of three variables: minimize the maximum reservoir level (return period of 1000 years), minimize the maximum released outflow (return period of 500 years) and maximize the reservoir yield with 90% reliability. We applied the methodology to Riaño Dam, jointly operated for irrigation and flood control. Improvements in the maximum reservoir yield (with 90% reliability) increased up to 10.1% with respect to the currently supplied annual demand (545 hm3) for the same level of dam and downstream hydrological safety. The improvement could increase up to 26.8% when compared to deterministic procedures. Moreover, dam stakeholders can select from a set of Pareto-optimal configurations depending on if their main emphasis is to maintain/increase the hydrological safety, or rather to maintain/increase the reservoir yield.

Cascaded Hydropower Operation Chart Optimization Balancing Overall Ecological Benefits and Ecological Conservation in Hydrological Extremes Under Climate Change

In this study, we proposed a new method to derive multi-purpose reservoir operation chart with the objectives of maximizing power generation and ecological conservation and minimizing ecological damage under future climate change. This double-parameter ecological operation chart (DEOC) was obtained by incorporating limited ecological curves into original conventional operation chart (COC) and then establishing and solving the operation optimization model considering both ecological profits and hydropower generation. Unlike COC where reservoirs are operated based on the current water level, reservoir operation under DEOC is based on both current reservoir water level and inflow. A case study was conducted in Jasajiang (JS) and Madushan (MDS) cascade reservoirs on the Yuan River in southwestern China. The monthly runoff data for the period 2021–2050 under RCP4.5 and RCP8.5 was simulated using the Soil and Water Assessment Tool (SWAT) driven by the General Circulation Models (GCMs), and the optimal ecological flow range calculated by PHABSIM for a given river section was used to determine the limited ecological curve. The operation optimization model considering both ecological profits and hydropower generation was established and solved to obtain DEOC. Compared with COC, DEOC results in a significant increase in power generation (>9%) and a reduction in ecological damage frequency without affecting the overall ecological conservation rate, especially at an ecological target of 70% and 80%, which can improve the adaptation of reservoir operation to climate change.

Dynamic rule curves for multipurpose reservoir operation for different floods

Abstract Dynamic flood control rule curves (DFCRCs) that balance the use of conservation space between flood control and conservation purposes are usually necessary for the operation of a multipurpose reservoir. This paper therefore proposes a procedure to apply the DFCRCs of an historically-based actual flood for reservoir operation versus different floods whose hydrograph shapes vary widely. The proposal uses related-size characteristics (e.g. net retention of the DFCRCs and peak discharge of associated outflows) in a stepwise manner from those of the smallest return period to those of the largest one. Illustrative applications of the procedure to the operation of the multipurpose Ubol Ratana Dam (The Nam Pong Basin, Thailand) have indicated that it enables the DFCRCs to ensure the reservoir's operation against various floods. Its operational results for the large and moderate floods in 1990 and 1995 are comparable to those of historically based floods. In addition, impact assessment of climate change on the operational performance has shown that the system could not protect the areas upstream and downstream of the dam from the HadCM3A2 and HadCM3B2 floods at the 80th and 95th percentile levels during the future 2050s period.

A Two-Stage Stochastic Optimization for Robust Operation of Multipurpose Reservoirs

Robust reservoir operation has long been considered a promising solution for addressing water allocation problems in the absence of reliable hydroclimatic forecasts. This study aims to evaluate the performance of this solution using a novel two-stage stochastic optimization model. The model maximizes economic benefits from reservoir deliveries while integrating stochastic inflows into a water allocation system with multiple demands and various constraints. The outcome of the model is a robust set of monthly reservoir releases that perform well under a wide range of hydroclimatic conditions. The model has been applied to the case of the Big Bend Reach of the Rio Grande/Bravo, a transboundary river basin of high importance for Mexico and the United States. The performance of the robust operation policy was assessed by comparing its outcome to those obtained under observed historical operations and an operation policy derived from a deterministic version of the optimization model that assumes perfect hydroclimatic knowledge. The results of this study indicate that the set of robust releases developed here outperforms historical reservoir operations and performs similarly to operations under perfect knowledge. These results show the effectiveness of robust reservoir operation and the usefulness of the proposed optimization model for decision-making under increasing hydroclimatic uncertainty.

Multi-objective optimisation using cellular automata: application to multi-purpose reservoir operation

ABSTRACT In this paper, a weighted cellular automata (CA) is proposed to solve bi-objective reservoir operation optimisation problem considering two objectives of water supply and hydropower production. A mathematically derived updating rule is used contributing to the efficiency of the proposed CA method. The updating rule of the problem is derived by converting the bi-objective problem to a single-objective problem using the well-known weighting method. The proposed method is used to operate the Dez reservoir in Iran over various operation periods of 60, 120, 240 and 480 months to test the performance of the method for operational problems of different scales. Performance of the method is also compared with that of a non-dominated sorting genetic algorithm (NSGAII) as one of the most popular multi-objective evolutionary algorithms. The results indicate that the proposed method is highly efficient compared to the NSGAII while producing comparable results. This is in line with the early findings of superior efficiency and comparable effectiveness of the CA method with the existing evolutionary algorithms for single objective optimisation problems.

Active future rule curves for multi-purpose reservoir operation on the impact of climate and land use changes

Future optimal rule curves are required for operating reservoir under uncertainty situation. This study applied the genetic algorithm (GA) connected a reservoir simulation model with smoothing function and adjusting of expert participation to search for future optimal reservoir rule curves for the Ubolrat Reservoir, which is located in the Northeast of Thailand, in period 2015–2064. The future optimal rule curves considered the impact of climate change with the PRECIS model under two emission scenarios, A2 and B2, and future land use maps using the CA Markov model. The future streamflow into the reservoir was determined using the Soil and Water Assessment Tool (SWAT) model. The results showed that the average future streamflow for the A2 and B2 were increased in comparison to the baseline year (1997–2014) due to the increase of average rainfall and temperatures, including widest land use change from rice and forest area to the sugarcane. The smoothing function applied to be the constraint can reduce the fluctuation of the upper and lower obtained rule curves from the GA. Further, the future rule curves can mitigate the frequency of water shortage situations and the releases of excess water during the increasing streamflow impacted by both climate scenarios and land use changes situation more than the existing and historic rule curves. Moreover, adjusted future rule curves with the expert participation were acceptable to operate the reservoir.