Journal of Water Resources Planning and Management

This paper develops a short-term model for the optimal operation of hydropower reservoirs. The model incorporates Muskingum channel routing, flow ramping constraints attributable to navigation requirements, and constraints on turbine operating regions, up/down time, and startup number. Lagrange multipliers are introduced to decompose the primal problem into a hydro subproblem and many individual unit subproblems, which are solved by the improved simplexlike method (SLM) and dynamic programming (DP). The Lagrange multipliers in the dual problem are updated by using an improved subgradient method, and the dual solution is modified to be feasible to the primal problem by using an efficient iterative policy. The method proposed has advantages in dealing with multiple identical units by defining united-generators to give a lower dimension and less execution time of the hydro subproblem. In a case study, the derived solution is very close to the optimal one, with a distance in benefit less than 0.55%.

This paper formulates a short-term operation problem to determine the optimal quarter-hourly generation schedules of multiple hydropower reservoirs with pumped-storage plants. The model is a comprehensive one that incorporates the plant-based operating zones (PBOZ), the upper and lower bounds on hydrowide power, the hydraulic coupling, and the requirements for generating power and releasing water with a fluctuation tolerance. A new procedure that includes three key techniques makes the problem easier to solve. First, the original problem is temporally decomposed into several quarter-hourly reservoir operation subproblems by dividing the planning horizon into subhorizons. Second, the plant-based operating zones that are integer variables are separately determined with dynamic programming (DP) after solving an hourly reservoir operation problem, which also derives a target-guiding trajectory for the subproblems. Third, iteratively linearizing the constraints simplifies the problems and having nonlinear objective functions guarantees the convergence of the procedure. As part of the software developed for Wuling Power Cooperation, Ltd., the present model and procedure are practical and efficient in aiding users in operating the reservoirs and scheduling the hydropower generation.

This study suggests a method for precise assessment of the effects of flow regulation by dams on downstream flow regimes, using a daily runoff simulation model. The soil and water assessment tool (SWAT) was used to evaluate the effects of flow regulation by upstream Soyanggang and Chungju multipurpose dams on downstream flow regimes at the inlet of Paldang Dam, a major water source of the Seoul metropolitan area in the Han River basin of Korea. The model was calibrated by using observed regulated inflows to Paldang Dam. The flow duration curves for the inflows and outflows of the Soyanggang and Chungju dams were used to evaluate the effects of storage of the two dams. In the simulation, Soyanggang Dam stored $0.74\mathrm{billion}{\mathrm{m}}^3$ of water during a 46-day high-flow period, with this volume then released during the 319 days of the low-flow period. Similarly, Chungju Dam was evaluated as storing $1.03\mathrm{billion}{\mathrm{m}}^3$ of water during a 19-day period, with this stored water then released during a 346-day period. To evaluate the effects of regulation by the two dams on downstream flow regimes, regulated and unregulated flow duration curves were constructed and analyzed. At the inlet of Paldang Dam, the regulated volume (i.e, the surplus water attributable to the regulation of the dam) by Soyanggang Dam, Chungju Dam, and both dams was 0.91, 1.25, and $2.16\mathrm{billion}{\mathrm{m}}^3$ , respectively, with regulation durations of 302, 337, and 311 days, respectively. This method accurately reconstructed the flow duration curves and then successfully evaluated the effects of regulation by the dams on downstream flow regimes. The method is the first to accurately reconstruct the natural flow regime, and the results of this study will likely be useful in evaluating the effects of flow regulation, as well as in water resources planning and management.

Climate change effects on combined sewer systems efficiency is a great matter of concern. In fact, changes in rainfall regime could significantly affect combined sewer overflows (CSOs) into receiving water bodies. Given that CSOs are a significant source of pollution, a better understanding and modeling of climate change effects on urban drainage systems is a compelling requirement to support design of adaptation strategies. This paper aims at studying the resilience of storm water detention tanks efficiency with respect to changes in rainfall forcing. In detail, an analytical probabilistic model is proposed to assess overflow reduction efficiency and volumetric efficiency of detention tanks depending on behaviors of climate and urban catchment. Sensitivity of tank efficiencies is evaluated under assigned changes in rainfall forcing. Results show that resilience of storm tanks benefits from filtering of climate change effects operated by the urban catchment.

The following work presents a multivariate statistical technique applied to the control of water inflows into district metering areas (DMAs) of urban networks. This technique, called principal-component analysis (PCA), allows for a sensitive and quick analysis of the inflows into a DMA without hassling mathematical algorithms. The PCA technique simplifies the original set of flow rate data recorded by the supervisory control and data acquisition (SCADA) system, synthesizing the most significant information into a statistical model that is able to explain most of the behavior of the water distribution network. The PCA technique also allows for the establishment of control charts that help system operators in the identification of anomalous behaviors regarding water use, bursts, or illegal connections. The described technique has been proven to offer high detection sensitivity to bursts or other unexpected consumptions.

This paper proposes a technique for the detection of extended blockages in pressurized water pipelines by using the system frequency responses under transient conditions. The impact of extended blockages on the system frequency responses is illustrated by using the analytical transfer matrix method, and the results indicate that extended blockages cause the resonant frequencies of the system to shift, and the blockage locations and sizes can be determined by analyzing the occurrences of the resonant peaks in the frequency axis. The analytical derivations of the blockage effect on the resonant frequencies are validated through different numerical experiments, and the applied results demonstrate that the extended blockages in pipeline can be detected by the proposed method as long as the system resonant frequencies are accurately determined. Practical implementations of the findings are discussed at the end of this paper.

Real-time sensing in water distribution systems provides a potentially powerful analytical tool for providing water security. Through monitoring surrogate parameters (e.g., pH, turbidity, and residual chlorine) over time, the natural variations of a distribution system’s parameters are established, allowing rapid detection of changes in water quality. However, the level of performance that water quality event detection algorithms have exhibited to date is insufficient for real-world utilization. Bayesian belief networks (BBNs) offer a formalized method of reasoning under uncertainty and are well suited to the analysis of multiple sources of information. The application of a BBN to water quality event detection is described. Surrogate parameters (pH, conductivity, and turbidity) were monitored during an experimental E. coli contamination. Difference filtration using a 60-s moving window of observations identified rapid rates of change present in the surrogate parameter signals, demonstrated as responsive to contamination as simulated in bench-scale studies. A BBN was constructed to assimilate the surrogate parameter variation and compute temporal probability distributions about the contamination of an experimental system. The BBN topology, probability distributions and data transformation techniques applied were validated through successful identification of contaminant injections.

This study presents a hybrid approach consisting of artificial neural network (ANN), fuzzy linear regression (FLR), and analysis of variance (ANOVA) for improvement of water consumption forecasting. Hence, this approach can be easily applied to uncertain or certain, or complex environments given its flexibility. The proposed hybrid approach is applied to forecast short-term water consumption in Tehran, Iran from April 5, 2004, to March 21, 2009. In this study, daily water consumption is viewed as the resultant of future and historical meteorological data. Implementation of the hybrid approach in a large metropolitan city such as Tehran seems to be ideal because of potential nonlinearity and uncertainty in the water consumption function of Tehran, Iran. The results of mean absolute percentage error (MAPE) indicate that selected ANN outperforms selected FLR on warm days. However, both ANN and FLR are ideal for cold days. To verify and validate the results, a sensitivity analysis is carried out by changing the train and test data sets. Finally, the comparison of the MAPE results of the hybrid approach with conventional linear regression confirms its considerable superiority for both warm and cold days.