Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia

In planning and managing water resources, the implementation of optimization techniques in the operation of reservoirs has become an important focus. An optimal reservoir operating policy should take into consideration the uncertainty associated with uncontrolled reservoir inflows. The charged system search (CSS) algorithm model is developed in the present study to achieve optimum operating policy for the current reservoir. The aim of the model is to minimize the cost of system performance, which is the sum of square deviations from the distinction between the release of the target and the actual demand. The decision variable is the release of a reservoir with an initial volume of storage, reservoir inflow, and final volume of storage for a given period. Historical rainfall data is used to approximate the inflow volume. The charged system search (CSS) is developed by utilizing a spreadsheet model to simulate and perform optimization. The model gives the steady-state probabilities of reservoir storage as output. The model is applied to the reservoir of Klang Gates for the development of an optimal reservoir operating policy. The steady-state optimal operating system is used in this model.

Characterizing Dam Induced Flood at Downstream of a Hydel Project

<p>Flooding due to sudden release from a hydropower dam during monsoon is becoming a serious concern for downstream locality, especially when there is lack of coordination between the dam authority and the Disaster Management Authority (DMA) at downstream. For hilly river, a disastrous flash flood is generally caused by short duration high intensity precipitation and a pondage hydropower project cannot attenuate such flood. Generally, reservoir simulation/optimization for a hydropower project is carried out on monthly, ten-daily or at best on daily basis to determine the best operating policy and to analyze impact of such operation on the flow scenario and therefore, in conventional analysis such flash flood event goes un-noticed. A detailed investigation of the downstream flooding is required before the construction of any hydropower project with at least on hourly basis to get insight into the impact of such inflow at downstream. Non-availability of short duration precipitation/flow data in interior project area, particularly in developing country hinder such analysis. Need and scope of such analysis is demonstrated by using a typical flow hydrograph of 48 hours, having two flood peaks, as inflow to the Lower Subansiri Hydroelectric Project (LSHP). The project is located in the Subansiri River, the largest tributary of the Brahmaputra River in India. Two operating policies; i) Standard Operating Policy (SOP) and ii) Dynamic Programming (DP) generated operating policy have been tested and both the polices have generated similar hourly flow time series of total reservoir outflow (spill + Release). These reservoir operation models have been coupled with the hydrodynamic model to route the hourly reservoir outflow from LSHP to a flood prone area located 13Km downstream of it. Post dam flood scenario thus generated is compared with the pre dam flood scenario by routing the same inflow hydrograph without considering the dam. As the river has an embankment, and flooding occurs only when the embankment fails, a specified water level at the downstream section has been considered as critical for flooding for the purpose of a comparative study.  For the considered inflow hydrograph, it is observed that the flood magnitude is not increased by the action of dam operation rather peaks get slightly attenuated. However, in natural condition without dam, flood rises gradually providing prior information to the locality and providing sufficient time for completing pre-disaster actions based on experience. With inclusion of dam, peak flow rises vary rapidly from a very low flow without showing any indication of flood beforehand and thus flood becomes more disastrous. Sudden fluctuation of water level can also cause failure of river bank and progressive bank failure can eventually cause the embankment to fail. The analysis has shown the possible impact of hydel project with more clarity to help disaster manager prepare mitigation measures in an informed way.</p>

Incorporating Outsourcing Strategy and Quality Assurance into a Multiproduct Manufacturer–Retailer Coordination Replenishing Decision

This study explores the multiproduct manufacturer-retailer coordination replenishing decision featuring outsourcing strategy and product quality assurance. Globalization has generated enormous opportunities. Consequently, transnational firms now face tough competition in global markets. To stay competitive, a firm should meet the client’s multi-item and quality requirements under capacity constraints and optimize the intra-supply chain system to allow the timely distribution of finished goods under minimum system cost. The outsourcing option is considered to release machine loadings and reduce cycle time effectively. All items fabricated are screened for quality, and reworkable and scrap items are separated. Any reworked items that fail the quality reassurance screening are discarded, whereas all outsourced products are quality-guaranteed by the provider. A fixed-quantity multi-shipment plan is used when the whole finished lot is quality-ensured to help present-day transnational firms gain competitive advantage by making efficient and cost-effective multiproduct manufacturing and delivering decisions. Mathematical modeling is built to portray the system’s characteristics, and conventional differential calculus is used to solve and derive the optimal operating policy for the proposed problem. Simultaneously, we find the optimal delivery frequency and common cycle time for the problem mentioned above. A simulated numerical example and sensitivity analysis demonstrate the research result’s capability and applicability. Our precise analytical model can reveal/highlight the impact of deviations in quality- and outsourcing-related features on the optimal operating policy and several performance indicators that help managerial decision-making.

Assessing the value of hydroclimatic services for hydropower megadams: the case of the Grand Ethiopian Renaissance Dam

<p>The recent advances in the skill of hydroclimatic services are motivating the need for quantifying their value in informing decisions. State-of-the-art forecasts proved to be skillful over seasonal and longer time scales especially in regions where climate teleconnections, such as El Nino Southern Oscillation, or particular hydrological characteristics, such as snow- and/or baseflow-dominance, enable predictability over such long lead times. Recent studies have investigated the value of seasonal streamflow forecasts in informing the operations of water systems in order to improve reservoir management strategies. However, how to best inform the operations of hydropower systems is still an open question because hydropower reservoir operations benefit from hydroclimatic services over a broad range of time scales, from short-term to seasonal and decadal time horizons, for combining daily and sub-daily operational decisions with strategic planning on the medium- to long- term.</p><p>In this work, we propose a machine-learning based framework to quantify the value of hydroclimatic services as their contribution to increasing the hydropower production of the Grand Ethiopian Renaissance Dam (GERD) in Ethiopia. The GERD, with an installed capacity of more than 6,000 MW is considered the largest hydroelectric power plant in Africa and the seventh largest in the world. Its construction is part of the strategic hydropower development plan in Ethiopia that aims to serve the growing domestic and foreign electricity demands. The quantification of the forecasts value relies on the Information Selection Assessment framework, which is applied to a service based on bias adjusted ECMWF SEAS5 seasonal forecasts used as input to the World-wide HYPE hydrological model. First, we evaluate the expected value of perfect information as the potential maximum improvement of a baseline operating policy relying on a basic information with respect to an ideal operating policy designed under the assumption of perfect knowledge of future conditions. Second, we select the most informative lead times of inflow forecast by employing input variable selection techniques, namely the Iterative Input Selection algorithm. Finally, we assess the expected value of sample Information as the performance improvement that could be achieved when the inflow forecast for the selected lead time is used to inform operational decisions. In addition, we analyze the potential value of forecast information under different future climate scenarios.</p><p>Preliminary results show that the maximum space for increasing the hydropower production of the GERD baseline operations not informed by any forecast is relatively small. This potential gain becomes larger when we focus on the performance during the heavy rainy season from June to September (Kiremt season), making room for the uptake of forecast information. The added production obtained with the forecast-informed operations of the GERD may represent an additional option in the current negotiations about the dam impacts on the downstream countries.</p><p> </p>

Optimization of Reservoir Operation using Linear Programming

The paper aims to derive the optimal releases monthly through linear programming for a single purpose reservoir. The releases from the reservoir are usually based upon the rule curves or operating policy adopted. The rule curve is the storage, indicating the water levels to be maintained in-order to satisfy the demand during the operation period. Linear programming (LP) is one of the global optimization techniques that have gained popularity as a means to attain reservoir operation. In the present study Linear Programming was used to develop an operation policy for Hemavathy Reservoir, Hassan District Karnataka, India. The decision variables were monthly reservoir releases for irrigation and initial storages in reservoir at beginning of the month. The constraint bound for the reservoir releases was reservoir storage capacity. The results derived by using Linear Programming shows that the downstream irrigation demands were satisfied and also considerable amount of water was conserved from reduced spills.

Machine Learning, Urban Water Resources Management and Operating Policy

Meticulously analyzing all contemporaneous conditions and available options before taking operations decisions regarding the management of the urban water resources is a necessary step owing to water scarcity. More often than not, this analysis is challenging because of the uncertainty regarding inflows to the system. The most common approach to account for this uncertainty is to combine the Bayesian decision theory with the dynamic programming optimization method. However, dynamic programming is plagued by the curse of dimensionality, that is, the complexity of the method is proportional to the number of discretized possible system states raised to the power of the number of reservoirs. Furthermore, classical statistics does not consistently represent the stochastic structure of the inflows (see persistence). To avoid these problems, this study will employ an appropriate stochastic model to produce synthetic time-series with long-term persistence, optimize the system employing a network flow programming modelling, and use the optimization results for training a feedforward neural network (FFN). This trained FFN alone can serve as a decision support tool that describes not only reservoir releases but also how to operate the entire water supply system. This methodology is applied in a simplified representation of the Athens water supply system, and the results suggest that the FFN is capable of successfully operating the system according to a predefined operating policy.