OPTIMAL OPERATION OF MULTIPURPOSE RESERVOIRS IN SERIES: ROSEIRES AND SENNAR CASE STUDY

The Roseires-Sennar Dams System (RSDS) at lower part of Blue Nile River play a vital role in water supply to the irrigation schemes in Sudan. The existing rule curves for this system belong to 1925 and 1966 for Sennar and Roseires reservoirs, respectively. Introduction of new irrigation schemes, approved climate change impacts on Blue Nile River flow and upstream developments in Ethiopia as well as the heightening of the Roseires Dam from elevation 480 to 490 m.a.s.l have shown the RSDS is losing its efficiency in terms of fully supplying the water demands. The literature addresses the simulation of Roseires and Sennar dams, and tries to find the best coordinated rule curves through a limited number of operation rules to find optimal operating rules for reservoirs that minimize the impacts of new developments, water demand growth and climate change on water supply to various demands on Blue Nile River. Such decisions are locally optimal in best condition since they do not consider the storage and carry-over capability of reservoirs that can transfer the non-optimal (locally optimal) decisions to other time steps of planning horizon and creat shortages in other time steps. Therefore, aim of this research is to find optimal coordinating operation rules for Roseires and Sennar dams that through a non-linear multi-period optimization model that considers the conditions of climate change, flow regime and water demand as scenarios. Model is validated by comparison with observed reservoir operation during November 1999 till May 2000. Eighteen scenarios that cover the normal, dry and very dry flow regimes, along with three suggested crop patterns and climate change impact are analyzed. Results shows in normal conditions of flow, crop pattern 2 is the most recommended with more than 11 Billion USD marginal profit and fully supplying the water demand and 1530 GWh energy generation per annum. The coordinated rule curves have a totally different pattern of emptying and filling compared with existing ones. Rule curves change from one flow regime to another, which proves how change in conditions of the system has influence on optimal operation rules. Comparison of marginal profits with crop pattern 2 shows in three inflow conditions of normal, dry and very dry years multi-period optimization model could keep the marginal profits above 11 Billion USD, let’s say, 11,050, 11,056 and 11,042 Billion USD, respectively, which shows the robustness of model in dealing with all conditions and keeping the marginal profits not affected. However, the Roseires rule curves are different in these three condition, while Sennar rules curves are almost the same. Without climate change impact, model can manage to supply the water demands fully in all flow conditions. However, water supply reliability is affected by climate change with all crop patterns. Roseires-Sennar Dams system in a normal year under climate change can produce 10,688 Billion USD marginal profit and 1371 GWh per year energy. It shows that model could manage the system performance so that climate change decrease the marginal profit by 3.27%, while inflow is reduced by 25% and water demands and evaporation increased by 19%. Energy generation under climate change has decreased by 10.5%, which is the most affected sector. Crop pattern 2 and 3 are not suitable for climate change conditions since up to 65% deficit in water supply can happen if very dry year realizing with climate change. In very dry conditions crop pattern 1 is more suitable to be practiced.

Deriving reservoir operating rules considering ecological demands of multiple stations

Reservoirs play important roles in hydropower generation, flood control, water supply, and navigation. However, the regulation of reservoirs is challenged due to their adverse influences on river ecosystems. This study uses ecoflow as an ecological indicator for reservoir operation to indicate the extent of natural flow alteration. Three reservoir optimization models are established to derive ecological operating rule curves. Model 1 only considers the maximization of average annual hydropower generation and the assurance rate of hydropower generation. Model 2 incorporates ecological objectives and constraints. Model 3 not only considers the hydropower objectives but also simulates the runoff and calculates the ecological indicator values of multiple downstream stations. The three models are optimized by a simulation-optimization framework. The reservoir ecological operating rule curves are derived for the case study of China's Three Gorges Reservoir. The results represent feasible schemes for reservoir operation by considering both hydropower and ecological demands. The average annual power generation and assurance rate of a preferred optimized scheme for Model 3 are increased by 1.06% and 2.50%, respectively. Furthermore, ecological benefits of the three hydrologic stations are also improved. In summary, the ecological indicator ecoflow and optimization models could be helpful for reservoir ecological operations.

Investigation of Forecast Accuracy and its Impact on the Efficiency of Data-Driven Forecast-Based Reservoir Operating Rules

Today, variable flow pattern, which uses static rule curves, is considered one of the challenges of reservoir operation. One way to overcome this problem is to develop forecast-based rule curves. However, managers must have an estimate of the influence of forecast accuracy on operation performance due to the intrinsic limitations of forecast models. This study attempts to develop a forecast model and investigate the effects of the corresponding accuracy on the operation performance of two conventional rule curves. To develop a forecast model, two methods according to autocorrelation and wrapper-based feature selection models are introduced to deal with the wavelet components of inflow. Finally, the operation performances of two polynomial and hedging rule curves are investigated using forecasted and actual inflows. The results of applying the model to the Dez reservoir in Iran visualized that a 4% improvement in the correlation coefficient of the coupled forecast model could reduce the relative deficit of the polynomial rule curve by 8.1%. Moreover, with 2% and 10% improvement in the Willmott and Nash—Sutcliffe indices, the same 8.1% reduction in the relative deficit can be expected. Similar results are observed for hedging rules where increasing forecast accuracy decreased the relative deficit by 15.5%. In general, it was concluded that hedging rule curves are more sensitive to forecast accuracy than polynomial rule curves are.

Hybridizing ANN-NSGA-II Model with Genetic Programming Method for Reservoir Operation Rule Curve Determination (Case Study Zayandehroud Dam Reservoir)

One of the most important and effective works of water resource planning and management is determining the specific, applicable, regulated operating policies of the Zayandehroud dam reservoir, as a case study, in which it should be user-friendly and straightforward for the operator. For this purpose, different methods have been proposed in which each of them has its limitations. Due to the unique capabilities of the genetic programming (GP) model, here, this method is used to determine the operating rule curves and policies of the dam reservoir. For this purpose, here, two cases are proposed in which, in the first case, each month is individually simulated and modeled. However, in the second case, all months are simulated simultaneously. A second case is proposed here to determine simple and more applicable operation rule curves. In addition, two approaches are suggested for each case in which in the first approach, the influential input variables are selected by presenting the hybrid method. In the proposed hybrid method, the artificial neural network (ANN) model is equipped with non dominated sorting genetic (NSGA-II) algorithm leading to a hybrid method named the ANN-NSGA-II method. However, in the second approach, the influential input variables are selected automatically using the GP method. Here, the hybrid method is proposed and used to overcome the limitations of existing usual method. In other words, it is proposed to reduce the number of influential input variables of data-driven methods and select the effective ones. The obtained results of all proposed cases and approaches are presented and compared with the standard operation policy (SOP) method, stochastic dynamic programming (SDP), ANN model and, NLP method. Comparison of the results shows the acceptable performance of the proposed cases and approaches. In other words, the best- obtained values of (stability index) SI index and water deficit (objective function value) are 49.3% and 32, respectively.

Regionalization of Reservoir regulation parameters using physiographic and climatological predictors

<p>Present regional and global scale hydrology has to account for man-made reservoirs that impart significant regulation signature into the downstream streamflow regime. Optimization of domains with large number of reservoirs would incur multitude of reservoir regulation parameters. Such parameter-set-per-reservoir approach not only results in excessive computational costs but also, by principle, lacks effective constraining of the parameter space. We propose an approach to derive single set of parameters for all the reservoirs and lakes in the modelling domain. The hypothesis is that reservoir regulation parameters can be regionalized using physiography and climatology at lakes and their catchments.<br><br>To test this hypothesis, we setup a modeling domain for the São Francisco basin of Northeast Brazil in the mesoscale hydrological model (mHM, www.ufz.de/mhm). The domain consists of climatology ranging from tropical (As) to semi-arid (BSh) and reservoirs with catchment area varying from less than 500 km<sup>2</sup> to greater than 500,000 km<sup>2</sup>. We carried out correlation analysis between selected physiographical and climatological predictors and the reservoir parameters of the multiscale lake module, mLM, of the mHM model (https://presentations.copernicus.org/EGU2020/EGU2020-6047_presentation.pdf). For an instance, the reservoir rule curves in mLM are estimated based on inflow and position of water level. The predictors here are inflow and water level which are normalized using catchment area and the shape of the reservoir, respectively. Similarly, the timing and shape parameters of rule curves were plotted against the climatological characteristics of the upstream catchment. The preliminary results reveal significant trends between the mLM parameters and the normalized predictors. These mathematical relationships, better known as transfer functions, can now be used to generate a single global reservoir parameter set.</p><p>The demonstrated hypothesis helps to optimize regulated hydrology using a single parameter set, irrespective of size, location and inherent climatology of reservoirs involved. This is inline with the pre-existing paradigm of multiscale parameter regionalization (MPR) of mHM. The findings contribute to the contemporary effort of hydrological modeling society towards improved global scale hydrological modeling.</p>

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.