Initial Storages Research Articles

Energy-Saving Deployment Algorithms of UAV Swarm for Sustainable Wireless Coverage

Recent years have witnessed increasingly more uses of Unmanned Aerial Vehicle (UAV) swarms for rapidly providing wireless coverage to ground users. Each UAV is constrained in its energy storage and wireless coverage, and it consumes most energy on flying to the top of the target area, thus leaving limited leftover energy for hovering at its deployed position and keeping wireless coverage. The literature largely overlooks the sustainability issues of deploying UAV swarm and the UAV-to-UAV cooperation, and we aim to maximize the minimum leftover energy storage among all the UAVs after their deployment. Our new energy-saving deployment problem captures that each UAV's wireless coverage is adjustable by its service altitude, and also takes the no-fly-zone (NFZ) constraint into account. We show that a UAV with larger initial energy storage in the UAV swarm should be deployed further away from the UAV station, and we propose an optimal energysaving deployment algorithm by jointly balancing heterogeneous UAVs' flying distances on the ground and final service altitudes in the sky. When n UAVs with different initial energy storages are dispatched from different initial locations, we prove this problem is NP-hard in general by reduction from partition problem. Despite of this, we propose to preserve the UAVs' location order and successfully design an approximation algorithm of complexity n log1/g to arbitrarily approach the optimum with error e. Inspiring by this proposed (1 - ε)-approximation algorithm, we present a heuristic algorithm to solve the general case by balancing the efficiency and computation complexity well.

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.

Establishment of cementation parameters of dried waste from evaporation coming from NPP operation

The radioactive wastes generated in Brazil are treated and sent to initial and intermediate storages. The “Project RBMN” proposes the implantation of the Brazilian repository to receive and permanently dispose the low and intermediate level radioactive wastes. The CNEN NN 6.09 standard – Acceptance Criteria for Disposal of Low and Intermediate Radioactive Wastes (LIRW) – establishes the fundamental requirements to accept the wastes packages in the repository. The evaporator concentrate is one of liquid wastes generated in a Nuclear Power Plant (NPP) operation and usually it is cemented directly inside the packing. The objective of this research is to increase the amount of the incorporated waste in each package, using the drying process before the cementation, consequently reducing the volume of the waste to be disposed. Drying and cementation parameters were established in order to scale-up the process aiming at waste products that comply with the requirements of CNEN standard. The cementation of the resulting dry wastes was carried out with different formulations, varying the amount of cement, dry waste and water. These tests were analyzed in order to select the best products, with higher waste incorporation than current process and its complying the requirements of the standard CNEN NN 6.09.

Drought mitigation under urbanization through an intelligent water allocation system

Due to the uneven spatio-temporal distribution of water resources and increasing water demands driven by urbanization, making effective water allocation is a critical and challenging issue nowadays. This study proposes an intelligent water allocation system (IWAS) to reduce the water pressure of multiple sectors and enhance the resilience of the water allocation system under urbanization during droughts. We first make forecasts on future water demands in response to urbanization for Taoyuan City in Taiwan by using system dynamics (SD) based on historical agricultural and industrial data as well as population statistics. We next design six water supply scenarios formed by a combination of ten-day inflow data collected from the Shihmen Reservoir in two drought years and three initial reservoir storages. The non-dominated sorting genetic algorithm-II (NSGA-II) is then used to search the minimal modified shortage index (MSI) and the maximal ratio of water storage to reservoir capacity (RWS) subject to future water demand forecasts under six designed drought scenarios. M-5 rule curves take care of water shortage simulation and serve as a benchmark. The results of the NSGA-II indicate that the improvement rates of MSI and RWS can reach up to 24% and 9.6%, respectively, as compared to those of M-5 rule curves. Furthermore, when we incorporate a great number of irrigation ponds spreading over the study area into the NSGA-II model, the improvement rates of MSI and RWS can increase by at most 35.5% and 1.5%, respectively, as compared to those of the NSGA-II without incorporating irrigation ponds. The results demonstrate that the proposed IWAS can greatly improve the effectiveness and advantages of water allocation, especially when irrigation ponds are considered as a water supply source, in response to future urban water demands under drought scenarios, and therefore provide decision makers with apt reference guidelines for sustainable water resources management.

A Multiobjective Stochastic Programming Model for Hydropower Hedging Operations under Inexact Information

This study develops a multiobjective stochastic programming model for informing hedging decisions for hydropower operations under an electricity market environment considering the benefit from selling energy production and the cost of penalizing energy shortfall. Aiming to determine the optimal strategy that hedges the risk of energy shortfall while keeping a high level of direct revenue from energy production under uncertain streamflows and inexact penalizing price conditions, competing objectives of minimizing energy shortfall percentage and maximizing direct revenue from energy production are analyzed. The conflict is resolved by determining the optimal level of energy shortfall percentage such that the net benefit of the hydropower system is maximized. The first-order optimality condition of maximized system net revenue is derived, which states that the marginal benefit of hedging equals the marginal cost of hedging at optimality. The tradeoff ratio between the competing objectives serves as the marginal cost of hedging and the penalizing price of energy shortfall represents the marginal benefit of hedging. Using the optimality condition, sensitivity tests are conducted for investigating the influence of different ranges of penalizing prices and reservoir initial storages on hedging decisions. The proposed method is evaluated on the operations of the Three Gorges cascade hydropower system during the drawdown season. Results show that: (1) minimizing the energy shortfall percentage adversely affects the maximization in system direct revenue from energy production, and the conflicting results are related to the depletion strategies of reservoir storage; (2) to reduce the energy shortfall percentage to the lowest level could result in significant reduction in total energy production and the direct revenue, especially when reservoir initial storages are low; and (3) the optimal level of energy shortfall percentage would decrease as penalizing price increases, when the influence of penalizing cost from energy shortfall gradually dominates the influence of energy production on the net revenue. The model framework and the implications could be applied to rationalize hedging decisions for hydropower operations under inexact information upon streamflow and penalizing prices.

Efficient discretization of state variables in stochastic dynamic programming model of Ukai reservoir, India

The monthly time step stochastic dynamic programming (SDP) model has been applied to derive the optimal operating policies of Ukai reservoir, a multipurpose reservoir in Tapi river basin, India. The initial reservoir storages and inflows into the reservoir in a particular month are considered as hydrological state variables. Since flood control and irrigation are the two major purposes of this reservoir, the SDP model is developed with the objective of minimizing annual sum of squared deviations of actual releases and actual storages from their respective target values. The uncertainty in the prediction of inflows into the reservoir system is addressed by incorporating the inflow transition probabilities. In this study, the effect of state variable discretization in deriving optimal operating policies using SDP for the reservoir is investigated. From the model results, it is inferred that, by adopting unequal interval storage discretization approach over equal interval storage discretization approach, there is an improvement of about 8–58% in the values of the objective function. The optimal operating policies derived using unequal interval storage discretization have been expressed in terms of final storages (levels) for each month for various combinations of inflows and initial storages.

Modelling Intelligent Water Resources Allocation for Multi-users

This study proposes intelligent water resources allocation strategies for multiple users through hybrid artificial intelligence techniques implemented for reservoir operation optimization and water shortage rate estimation. A two-fold scheme is developed for (1) knowledge acquisition through searching input–output patterns of optimal reservoir operation by optimization methods and (2) the inference system through mapping the current input pattern to estimate the water shortage rate by artificial neural networks (ANNs). The Shihmen Reservoir in northern Taiwan is the study case. We first design nine possible water demand conditions by investigating the changes in historical water supply. With the nine designed conditions and 44-year historical 10-day reservoir inflow data collected during the growth season (3 months) of the first paddy crop, we first conduct the optimization search of reservoir operation by using the non-dominated sorting genetic algorithm-II (NSGA-II) in consideration of agricultural and public water demands simultaneously. The simulation method is used as a comparative model to the NSGA-II. Results demonstrate that the NSGA-II can suitably search the optimal water allocation series and obtain much lower seasonal water shortage rates than those of the simulation method. Then seasonal water shortage rates in response to future water demands for both sectors are estimated by using the adaptive network fuzzy inference system (ANFIS). The back-propagation neural network (BPNN) is adopted as a comparative model to the ANFIS. During model construction, future water demands, predicted monthly inflows (or seasonal inflow) of the reservoir in the next coming quarter and historical initial reservoir storages configure the input patterns while the optimal seasonal water shortage rates obtained from the NSGA-II serve as output targets (training targets) for both neural networks. Results indicate that the ANFIS and the BPNN models produce almost equally good performance in estimating water shortage rates, yet the ANFIS model produces even better stability. The reliability of the proposed scheme is further examined by scenario analysis. The scenario analysis indicates that an increase in public water demand or a decrease in agricultural water demand would bring more impacts of water supply on agricultural sectors than public sectors. Similarly, a bigger decrease in inflow amount would obviously bring more influence on agricultural sectors than public one. Consequently, given predicted inflow, decision makers can pre-experience the possible outcomes in response to competing water demands through the estimation models in order to determine adequate water supply as well as preparedness measures, if needed, for drought mitigation.

Improved regional water management utilizing climate forecasts: An interbasin transfer model with a risk management framework

AbstractRegional water supply systems undergo surplus and deficit conditions due to differences in inflow characteristics as well as due to their seasonal demand patterns. This study proposes a framework for regional water management by proposing an interbasin transfer (IBT) model that uses climate‐information‐based inflow forecast for minimizing the deviations from the end‐of‐season target storage across the participating pools. Using the ensemble streamflow forecast, the IBT water allocation model was applied for two reservoir systems in the North Carolina Triangle Area. Results show that interbasin transfers initiated by the ensemble streamflow forecast could potentially improve the overall water supply reliability as the demand continues to grow in the Triangle Area. To further understand the utility of climate forecasts in facilitating IBT under different spatial correlation structures between inflows and between the initial storages of the two systems, a synthetic experiment was designed to evaluate the framework under inflow forecast having different skills. Findings from the synthetic study can be summarized as follows: (a) inflow forecasts combined with the proposed IBT optimization model provide improved allocation in comparison to the allocations obtained under the no‐transfer scenario as well as under transfers obtained with climatology; (b) spatial correlations between inflows and between initial storages among participating reservoirs could also influence the potential benefits that could be achieved through IBT; (c) IBT is particularly beneficial for systems that experience low correlations between inflows or between initial storages or on both attributes of the regional water supply system. Thus, if both infrastructure and permitting structures exist for promoting interbasin transfers, season‐ahead inflow forecasts could provide added benefits in forecasting surplus/deficit conditions among the participating pools in the regional water supply system.

Modelling soil organic carbon storage with RothC in irrigated Vertisols under cotton cropping systems in the sub-tropics

The performance of the Rothamsted Carbon Model (RothC) in simulating soil carbon (SOC) storage in cotton based cropping systems under different tillage management practices on an irrigated Vertisol in semi-arid, subtropics was evaluated using data from a long-term (1994–2012) cotton cropping systems experiment near Narrabri in north-western New South Wales, Australia. The experimental treatments were continuous cotton/conventional tillage (CC/CT), continuous cotton/minimum tillage (CC/MT), and cotton-wheat (Triticum aestivum L.) rotation/minimum tillage (CW/MT). Soil carbon (C) input was calculated by published functions that relate crop yield to soil C input. Measured values showed a loss in SOC of 34%, 24% and 31% of the initial SOC storages within 19 years (1994–2012) under CC/CT, CC/MT, and CW/MT, respectively. RothC satisfactorily simulated the dynamics of SOC in cotton based cropping systems under minimum tillage (CC/MT and CW/MT), whereas the model performance was poor under intensive conventional tillage (CC/CT). The model RothC overestimated SOC storage in cotton cropping under conventional intensive tillage management system. This over estimation could not be attributed to the overestimation of soil C inputs, or errors in initial quantification of SOC pools for model initialization, or the ratio of incoming decomposable plant materials to resistant plant materials. Among other different factors affecting SOC dynamics and its modelling under intensive tillage in tropics and sub-tropics, we conclude that factors for tillage and soil erosion might be needed when modelling SOC dynamics using RothC under intensive tillage management system in the tropics and the sub-tropics.

Water renewal time of the Yellow River mainstream based on reservoir action

This note analyzes the change in water renewal time characteristics based on reservoir action and then establishes calculation models for the water renewal time in the Yellow River mainstream. The results indicate that the amount of renewable water with reservoir action can meet the annual water demand and that water flows naturally at the Lijin station near estuary. Initial storage dynamics is an important factor in water resource renewable capacity at a certain time, and rational reservoir action can promote sustainable water resource utilization. When the initial storages in the Longyang Gorge reservoir are 9.343 and 5.343 billion m3, the water renewal times are 28 and 33.9 d, respectively. Flow stoppage appears in April and May.

Optimal crop planning using a chance constrained linear programming model

A monthly time-stepped chance constrained linear programming (CCLP) model was developed to derive optimal cropping patterns and optimal operational strategies for the Sri Ram Sagar Project, with stochastic inflows. The stochastic nature of the inflows was incorporated into the model in its equivalent deterministic form. These equivalent deterministic inflow values were estimated from the annual and monthly probability distribution of observed inflows, and named the chance constrained linear programming model-annual and chance constrained linear programming model-monthly, respectively. The models were solved for nine different dependable inflow levels, namely for 50, 55, 60, 65, 70, 75, 80, 85 and 90% in each CCLP. The results of the models were compared with respect to net benefit, irrigation intensity, total cropped area, optimal cropping pattern, optimal releases, evaporation loss and initial storages. Based on the results obtained, it can be concluded that, in CCLP optimization, the probability distribution of ‘model time period’ (t-month in this case) should be considered rather than the probability of ‘planning time period’ (year in this case).

Variability and persistence of hillslope initial conditions: A continuous perspective on subsurface flow response to rain events

► HYDRUS simulations examine event response to hillslope-scale initial conditions. ► Hillslope storage exhibits preferred ranges for wet and dry seasons. ► Hillslope field capacity separates wet and dry season preferred storage ranges. ► Subsurface stormflow requires an initial storage threshold near field capacity. ► Total subsurface stormflow is a nonlinear function of initial storage. Runoff response to rain events depends on the initial moisture conditions in the subsurface. This study explores subsurface stormflow response to initial conditions within the context of a continuous hillslope water balance. A hypothetical hillslope with three-dimensional variably saturated subsurface flow is developed using the HYDRUS model forced with a year-long sequence of hourly precipitation and transpiration from Seattle, WA, USA. Using six different soil hydraulic parameter sets, test simulations examine (1) variability of hillslope initial conditions prior to rain events, (2) persistence of initial conditions in a continuous simulation, and (3) effects of initial conditions on subsurface stormflow during rain events. Results show that hillslope initial conditions vary seasonally, producing bimodal distributions of storage values with preferred storage ranges for wet and dry seasons. Preferred storage ranges differ by soil texture and by hydraulic conductivity. Wet season initial conditions are most frequently at storage values above hillslope field capacity, with higher preferred ranges of storage for scenarios with lower saturated hydraulic conductivity. Dry season hillslope storage values converge to minimum values below field capacity, and these minimum values vary with soil texture but not with saturated hydraulic conductivity. Tests of initial condition convergence show that scenarios with different initial storages can eventually converge under either persistent wetting or persistent drying until hillslope storage reaches the wet or dry preferred states. Dry preferred states are unlikely to produce subsurface stormflow, as test rain events generate hillslope outflow only when initial storage is at or above an event and parameter-specific threshold near the hillslope field capacity. Above this flow threshold, all simulations show a nonlinear increase in subsurface stormflow with increasing initial storage, with the steepest rates of increase for scenarios with the highest values of saturated hydraulic conductivity. Simulation experiments present a quantitative approach for deriving functional relationships between event response, initial storage, and hillslope water retention.

Optimal Drought Management Using Sampling Stochastic Dynamic Programming with a Hedging Rule

This study develops procedures that calculate optimal water release curtailments during droughts using a future value function derived with a sampling stochastic dynamic programming model. Triggers that switch between a normal operating policy and an emergency operating policy (EOP) are based on initial reservoir storage values representing a 95% water supply reliability and an aggregate drought index that employs 6-month cumulative rainfall and 4-month cumulative streamflow. To verify the effectiveness of the method, a cross-validation scheme (using 2,100 combination sets) is employed to simulate the Geum River basin system in Korea. The simulation results demonstrate that the EOP approach: (1) reduces the maximum water shortage; (2) is most valuable when the initial storages of the drawdown period are low; and (3) is superior to other approaches when explicitly considering forecast uncertainty.

Improved Drought Management of Falls Lake Reservoir: Role of Multimodel Streamflow Forecasts in Setting up Restrictions

Droughts, resulting from natural variability in supply and from increased demand due to urbanization, have severe economic implications on local and regional water supply systems. In the context of short-term monthly to seasonal water management, predicting these supply variations well in advance are essential in advocating appropriate conservation measures before the onset of drought. In this study, we utilized 3-month ahead probabilistic multimodel streamflow forecasts developed using climatic information—sea surface temperature conditions in the tropical Pacific, tropical Atlantic, and over the North Carolina coast—to invoke restrictions for Falls Lake Reservoir in the Neuse River Basin, N.C. Multimodel streamflow forecasts developed from two single models, a parametric regression approach and semiparametric resampling approach, are forced with a reservoir management model that takes ensembles to estimate the reliability of meeting the water quality and water supply releases and the end of the season target storage. The analyses show that the entire seasonal releases for water supply and water quality uses could be met purely based on the initial storages 100% reliability of supply, thereby limiting the use of forecasts. The study suggests that, by constraining the end of the season target storage conditions being met with high probability, the climate information based streamflow forecasts could be utilized for invoking restrictions during below- normal inflow years. Further, multimodel forecasts perform better in detecting the below-normal inflow conditions in comparison to single model forecasts by reducing false alarms and missed targets which could improve public confidence in utilizing climate forecasts for developing proactive water management strategies.

Optimal Reservoir Operation Policies Using Genetic Algorithm

In reservoir operation, appropriate methodology for deriving reservoir operating rules should be selected and operating rules should then be formulated. In the present study Genetic Algorithm (GA) has been used to optimize the operation of existing multipurpose reservoir in India, and to derive reservoir operating rules for optimal reservoir operations. The fitness function used is minimizing the squared deviation of monthly irrigation demand along with the squared deviation in mass balance equation. The decision variables are monthly releases for irrigation from the reservoir and initial storages in reservoir at beginning of the month. The constraints considered for this optimization are reservoir capacity and the bounds for decision variables. Results show that, even during the low flow condition the GA model if applied to the Upper Wardha reservoir in Maharastra State, India, can satisfy downstream irrigation demand. Hence based on the present case study it can be concluded that GA model has the capability to perform efficiently, if applied in real world operation of the reservoir.

Optimal reservoir operation for irrigation of multiple crops using elitist-mutated particle swarm optimization

To achieve social and economic sustainability in arid and semi-arid areas under water scarce situations, it is vital to promote efficient use of water through improved management of water resources. This paper presents a swarm optimization based solution to a detailed operational model for short-term reservoir operation for irrigation of multiple crops. The model integrates the dynamics associated with the water released from a reservoir to the actual water utilized by crops at farm level. It takes into account the nonlinear relationship of root growth, soil heterogeneity, soil moisture dynamics for multiple crops, yield response to water deficit at various growth stages of the crops and economic benefits from the crops. As the developed model is a nonlinear one, it is solved using a novel global optimization technique, namely elitist-mutation particle swarm optimization (EMPSO). The model's applicability is demonstrated through a case study of Malaprabha Reservoir system in Southern India. The performance of the model is examined for different water deficit conditions and the sensitivity of the crop yield is analysed for water shortage at various growth stages. Also, the consideration of economic benefits in the objective function and its effect on the water allocation decisions for multiple crops are studied. Consequently, the output from the model includes initial storages, releases, overflows and evaporation losses for each 10-day period on the reservoir side; and allocation of water, actual evapotranspiration and initial soil moisture for each crop for each 10-day period on the field side, thus facilitating decision making for optimal utilization of the available water resources.

沈砂池における利水の可能性に関する実験的研究

In the Amami and Okinawa area, Japan, there are a great number of settling basins established in order that suspended sediments, which float from cultivated fields to rivers or sea areas, can be sedimented and removed. Currently, there has been an increase in the number of requests for water storage in these settling basins in cultivated fields with no irrigation reservoirs in order to utilize the water for irrigation. Since settling basins are utilized as a water source, water should always be stored in settling basins. Therefore, it is necessary to investigate how this water affects the function of settling turbid water in settling basins. This paper aims at experimentally investigating the feasibility of using a settling basin as a storage basin for irrigation water use. For this, two extremities of initial water storage in a model basin, i. e., empty and full storages, are considered. The turbidity (the concentration of suspended sediments) and the quantity of water leaving the basin from its outlet are measured to evaluate the amount of suspended sediments not trapped in the basin (i. e., escaping from the basin). Different tests with different initial water storages are comparatively conducted to estimate the effect of keeping the storage full for water use on the primary function of the basin. The results show that in both cases of initial water storage the equilibrium levels to which the outflow turbidities reach as time passes are the same. The amount of the escaping suspended sediments for the initially empty storage is slightly less than that for the initially full storage. It is thus concluded that the settling basin could be used as a storage basin for irrigation water use as well.