MODELING AND CONTROL OF THE POLLUTION OF WATER RESOURCES SYSTEMS VIA MULTILEVEL APPROACH1

ABSTRACT. The problem of modeling and control of water pollution is considered. A general mathematical model, where the pollution effluent is discharged directly into the river, into the lake, or into a bypass pipe leading to an advanced Waste Water Treatment (AWT) plant, is developed. The Water Resource System (WRS) under consideration is decomposed into N subsystems. The pollution effluent input vector to each subsystem includes the water quantity and different water characteristics such as BOD, DO, pH, conductivity, temperature, algae, phosphates, nitrates, etc. Treatment cost functions and quality transition functions as well as system model constraints are introduced, where all functions can be nonlinear. A system Lagrangian is formed to incorporate the system constraints and coupling. The Lagrangian is decomposed into N independent subsystems, and a two level optimization methodology is introduced. Each subsystem is independently and separately minimized at the first level assuming known Lagrange multipliers. At the second level, the total Lagrangian is maximized with respect to the Lagrange multipliers using optimal values for effluent inputs from all subsystems obtained from the first level. Economic interpretation on the Lagrange multipliers reveals that they are merely prices imposed by the central authority (second level) for the pollution caused by the subsystems. Advantages of the multilevel approach are discussed.     

Stormwater Control Measures for Runoff and Water Quality Management in Urban Landscapes

This study develops and tests a novel optimization method for optimally selecting and sizing stormwater control measures (SCMs) in urban landscapes for selected design storms. The developed methodology yields SCMs that capture and retain stormwater via onsite percolation, remove stormwater pollutants, and minimize stormwater control expenditures. The resulting environmental optimization problem involves integer and real variables imbedded in an objective function that is subjected to multiple constraints. This study's methodology aims at practicality and ease of implementation in the solution of the SCM sizing and selection optimization problem while taking into account the main factors that govern stormwater management in urban landscapes. The near‐optimal global solution of the SCM selection and design problem is obtained with nonlinear programming and verified with the average of multiple solutions calculated with multiple runs of an optimization evolutionary algorithm. The developed methodology is illustrated with one stormwater project in the City of Los Angeles, California.     

A NONLINEAR MODEL OF A WATER RESERVOIR SYSTEM WITH MULTIPLE USES AND ITS OPTIMIZATION BY COMBINED USE OF DYNAMIC PROGRAMMING AND PATTERN SEARCH TECHNIQUES

A fairly realistic nonlinear model of a water reservoir system with multiple uses has been developed based on available data, and the optimum of the system based on the developed model has been determined by the combined use of dynamic programming and the pattern search techniques. Both the simplex search and the Hooke and Jeeves pattern search have been used. The approach in modeling and optimization can treat complex inequality constraints. The benefits or losses resulting from four purposes or uses of water, namely, urban water supply, hydroelectric power generation, irrigation, and recreation, are taken into account in the profit function. Other uses such as flood control, navigation, and fish and wildlife enhancement are considered indirectly by the use of inequality constraints. It appears that the approach developed in this work can treat a water resource allocation problem involving complex inequality constraints.     

AQUIFER MANAGEMENT UNDER TRANSIENT AND STEADY-STATE CONDITIONS1

ABSTRACT: The equations of transient and steady-state flow in two-dimensional artesian aquifers are approximated using finite differences. The resulting linear difference equations, combined with other linear physical and management constraints and a linear objective function, comprise a linear programming (LP) formulation. Solutions of such LP models are used to determine optimal well distributions and pumping rates to meet given management objectives for a hypothetical transient problem and for a steady-state field problem.     

Nonlinear control of a wind turbine based on nonlinear estimation techniques for maximum power extraction

This work proposes nonlinear estimators with nonlinear controllers, for variable speed wind turbine (VSWT) considering that either the wind speed measurement is not available or not accurate. The main objective of this work is to maximize the energy capture from the wind and minimizes the transient load on the drive train. Controllers are designed to adjust the generated torque for maximum power output. Estimation of effective wind speed is required to achieve the above objectives. In this work the estimation of effective wind speed is done by using the Modified Newton Rapshon (MNR), Neural Network (NN) trained by different training algorithms and nonlinear time series based estimation. Initially the control strategies applied was the classical ATF (Aerodynamic torque feed forward) and ISC (Indirect speed control), however due their weak performance and unmodeled WT disturbances, nonlinear static and dynamic feedback linearization techniques with the above wind speed estimators are proposed.     

WATER RESOURCES MODELING AND OPTIMIZATION BASED ON CONSERVATION AND FLOODING POOLS1

ABSTRACT: A new and practical concept in water resources modeling and optimization is introduced. Instead of unrealistically assuming a multipurpose reservoir to be composed of a single lumped pool of water, it is treated as two different pools namely conservation and flood pools. Based on this treatment, the optimization problem is stated using the concepts of Lagrange multipliers and parameter optimization. The optimization problem consists of the material balance equation, the constraints on control and state variables and the objective function.     

Urban water resources planning by using a modified particle swarm optimization algorithm

A new optimization algorithm by coupling the mutation process to the particle swarm optimization (PSO) is developed in this paper. This algorithm, entitled particle swarm optimization with mutation similarity (PSOMS), is successfully applied to an urban water resources management problem for the large city of Tabriz, Iran. The objective functions of the optimization problem are to minimize the cost, maximize water supply and minimize the environmental hazards. The constraints are physical limits such as pipelines capacity, ground water, the demand and the impact of conservation tools. Due to the parameters uncertainty, the water supply objective is modeled with fuzzy set theory and the objectives are then combined with compromise programming. The resulted single objective is solved using PSOMS, and its efficiency is then compared with the basic PSO and two kinds of genetic algorithms. Among them, PSOMS shows rapid convergence and suitable results compared to other methods. PSOMS is also improved to provide the Pareto frontier, which is needed to proper selecting of the optimal solutions in the uncertain conditions. Finally, the diversity of solutions is checked based on an indicator of the distances between different solutions, which show the efficiency of the PSOMS algorithm with respect to the genetic algorithm. Then by using the non-symmetric Kalai–Smorodinsky method a guideline is provided for comfort selection of the most preferred solution in the Pareto frontier. Based on these outcomes, the multi-objective PSOMS provides more appropriate results needed for urban systems management.     

Optimization of a Wind/PV Hybrid Power Generation System

This study investigates the wind and solar electricity generation availability at the Solar Energy Institute of Ege University, Izmir, Turkey. The main purpose of this study is to design an appropriate wind-PV hybrid system to cover the electricity consumption of the Institute. In order to do this, monthly average solar irradiation and wind speed data are used, which were measured, consisting of hourly records over an eight-year period from 1995–2002. Simple models were developed to determine wind, solar, and hybrid power resources per unit area. Correlations between the solar and wind power data were carried out on an hourly, daily, and monthly basis. It is shown that the hybrid system can be applied for the efficient and economic utilization of these resources.     

REGIONAL WATER QUALITY MANAGEMENT BY THE GENERALIZED REDUCED GRADIENT METHOD1

ABSTRACT: A river basin-wide water quality management system is considered. The river receives thermal as well as organic wastes. At-source treatment of these pollutants is imposed to control the basin-wide water quality. The related water quality standards are: the minimum DO concentration, the maximum allowable BOD concentration, the maximum allowable stream temperature, and the allowable rise in stream temperature. The general dynamic mathematical model representing water quality in streams and the thermal effects on BOD and DO concentrations is presented. The model is highly nonlinear in nature. The optimal management problem involving the model is solved by a recently developed nonlinear propgramming technique - the generalized reduced gradient (GRG) method. Comparison of results obtained by the GRG method vs. dynamic programming, and of results using a more realistic mathematical model vs. a simple model are presented. The analysis procedure can be applied to designing new and examining existing water quality programs, and to study the influence of alternate policies and constraints.     

OPTIMIZATION OF WATER ALLOCATION DECISIONS AFFECTING ESTUARINE ECOLOGY1

ABSTRACT .The problem analyzed in this paper is how to allocate optimally the available surface water in a river system among those who compete for its use, while acknowledging explicitly that for coastal states the ecology of bays and estuaries must be numbered among the competitors. The objective is to maximize the benefit resulting from water use while satisfying a set of constraints on flow. Benefit is assumed to be a function of the amount of water used and the time period in which the water is used. A mathematical model of this problem is shown to fit the format of the minimum cost circulation network flow problem. The Out-of-Kilter algorithm of D. R. Fulkerson is proposed as a solution technique. Sensitivity analysis on the input data is described as a means of determining the minimum economic benefit required to justify the allocation of a given volume of water needed to sustain the ecology of an estuary.     

电源适配器平均效率的测量不确定度评定

对符合GB 20943-2013定义的电源适配器平均效率的测量不确定度进行了实例评定,示范了如何处理评定过程中复杂的非线性数学模型问题,阐述了获取电源适配器平均效率合成标准不确定度的过程和方法,进而确定电源适配器平均效率测量结果的扩展不确定度。     

PROCEDURE FOR THE ECONOMICAL EVALUATION OF WATER NETWORKS' PARAMETERS1

ABSTRACT: The Generalized Geometric Programming and Branch and Bound technique are used to reach optimally designed water systems with their variables in integer forms. The water systems and constraints are formulated by sets of nonlinear equations. A solution for a specific water system is presented.     

Flexible interval mixed-integer bi-infinite programming for environmental systems management under uncertainty

A number of inexact programming methods have been developed for municipal solid waste management under uncertainty. However, most of them do not allow the parameters in the objective and constraints of a programming problem to be functional intervals (i.e., the lower and upper bounds of the intervals are functions of impact factors). In this study, a flexible interval mixed-integer bi-infinite programming (FIMIBIP) method is developed in response to the above concern. A case study is also conducted; the solutions are then compared with those obtained from interval mixed-integer bi-infinite programming (IMIBIP) and fuzzy interval mixed-integer programming (FIMIP) methods. It is indicated that the solutions through FIMIBIP can provide decision support for cost-effectively diverting municipal solid waste, and for sizing, timing and siting the facilities’ expansion during the entire planning horizon. These schemes are more flexible than those identified through IMIBIP since the tolerance intervals are introduced to measure the level of constraints satisfaction. The FIMIBIP schemes may also be robust since the solutions are “globally-optimal” under all scenarios caused by the fluctuation of gas/energy prices, while the conventional ones are merely “locally-optimal” under a certain scenario.     

A COMPARISON OF TWO METHODS FOR MULTIOBJECTIVE OPTIMIZATION FOR RESERVOIR OPERATION1

ABSTRACT: Two major objectives in operating the multireservoir system of the Upper Colorado River basin are maximization of hydroelectric power production and maximization of the reliability of annual water supply. These two objectives conflict. Optimal operation of the reservoir system to achieve both is unattainable. This paper seeks the best compromise solution for an aggregated reservoir as a surrogate of the multireservoir system by using two methods: the constraint method and the method of combined stochastic and deterministic modeling. Both methods are used to derive the stationary optimal operating policy for the aggregated reservoir by using stochastic dynamic programming but with different objective functions and minimum monthly release constraints. The resulting operating policies are then used in simulated operation of the reservoir with historical inflow records to evaluate their relative effectiveness. The results show that the policy obtained from the combination method would yield more hydropower production and higher reliability of annual water supply than that from the constraint-method policy.     

Optimization of grid tied hybrid power system in smart premises

Tapping of renewable energy sources like solar and wind is given great priority by power producers all over the world. Technical problems of linking them to the grid are solved. The cost constraints of utilizing renewable energy at specific locations are to be determined. In this work, a model is developed for grid tied hybrid power system (HPS) consisting of photovoltaic (PV) module and wind mill at the roof top of smart premises. The grid is capable of delivering and receiving energy. Objective function is formed with constraints taking into account the cost of PV module, wind mill, and grid tied inverter with controller. The constraints are rating of HPS and energy that can be delivered to the grid. Using this model, case studies were conducted in three locations in India, each location having two different demands. The results are presented. With the optimal rating of HPS, results shows that, conventional energy cost is higher.     

Modeling urban solar energy with high spatiotemporal resolution: A case study in Toronto,Canada

This research presents a method to determine the maximum potential for the capturing of solar radiation on the rooftop of buildings in an urban environment. This involves the modeling of solar energy potential and comparison to historical building energy demand profiles through the use of 3-D solar simulation software tools and geographic information systems (GIS). The objective is to accurately identify the amount of surface area that is suitable for solar photovoltaic (PV) installations and to estimate the hourly PV electricity generation potential of existing building rooftops in an urban environment. This study demonstrates a viable approach for modeling urban solar energy and offers valuable information for electricity distributors, policy makers, and urban energy planners to facilitate the substantial design of a green built environment. The developed methodology is comprised of three main sections: (1) determination of suitable rooftop area, (2) determination of the amount of incident solar radiation available per rooftop, and (3) estimation of hourly solar PV electricity generation potential. A case study was performed using this method for Ryerson University, located in Toronto, Canada. It was found that solar PV could supply up to 19% of the study area’s electricity demands during peak consumption hours. The potential benefits of solar PV was also estimated based upon hourly greenhouse gas emission intensity factors as well as Time-of-Use (TOU) savings through the Ontario Feed-in-Tariff (FIT) program, which allows for better representation of the positive impacts of solar technologies.     

Attitudes of Dutch Pig Farmers Towards Tail Biting and Tail Docking

The Dutch policy objective of a fully sustainable livestock sector without mutilations by 2023 is not compatible with the routine practice of tail docking to minimize the risk of tail biting. To examine farmer attitudes towards docking, a telephone survey was conducted among 487 conventional and 33 organic Dutch pig farmers. “Biting” (of tails, ears, or limbs) was identified by the farmers as a main welfare problem in pig farming. About half of the farmers reported to have no tail biting problems in their own herd. When farmers did report problems, they most often reported figures between 1 and 5 % of the animals. High incidences of tail biting were anticipated when trying to keep undocked pigs. Enrichment materials used in the conventional sector included mainly chains (52–63 % of the farms) and hanging rubber or plastic balls (22–30 %). Straw, sawdust, or wood shavings was hardly provided in conventional pig farming (2–3 %), in contrast to organic farming (88–100 % of farms). Conventional pig farmers feel a curly tail is not very important for sustainable pig farming. They consider enrichment to be less effective and tail docking to be less stressful for them and their piglets than their organic colleagues do. Pig farmers identified climate as a main risk factor for tail biting as opposed to enrichment. The objective of reducing routine tail docking requires solutions for dealing with tail biting problems at the farm level. In this process, transfer of scientific knowledge about enrichment materials and other measures to prevent and cure tail biting is critical, as is a change in farmer attitudes and awareness of the moral issues involved.     

IMPACTS OF SURFACE DRAINAGE ON GROUND WATER HYDRAULICS1

ABSTRACT: The current dredge and fill practices in locating canals along the periphery of wetlands in south Florida are transforming natural basins that originally had primarily slower subsurface drainage to ones that discharge larger quantities of water faster, via a surface drainage system. The objective of this paper is to develop an analytical technique and a numerical model in quantifying the difference of surface and subsurface runoff before and after the construction of drainage canals, and for delineating the effects of drains on channel level and regional water tables in adjacent areas in south Florida. The surface runoff model is formulated on the climatic water balance technique, and the ground water model is treated as a one dimensional transient phenomenon that forms a nonlinear flow problem. Analytical solutions are derived through problem linearization. These two models are coupled to estimate the impact of drainage canals on the adjacent water table drawdown.     

Risk-based optimal operation of hybrid power system using multiobjective optimization

ABSTRACT

This paper solves an optimal generation scheduling problem of hybrid power system considering the risk factor due to uncertain/intermittent nature of renewable energy resources (RERs) and electric vehicles (EVs). The hybrid power system considered in this work includes thermal generating units, RERs such as wind and solar photovoltaic (PV) units, battery energy storage systems (BESSs) and electric vehicles (EVs). Here, the two objective functions are formulated, i.e., minimization of operating cost and system risk, to develop an optimum scheduling strategy of hybrid power system. The objective of proposed approach is to minimize operating cost and system risk levels simultaneously. The operating cost minimization objective consists of costs due to thermal generators, wind farms, solar PV units, EVs, BESSs, and adjustment cost due to uncertainties in RERs and EVs. In this work, Conditional Value at Risk (CVaR) is considered as the risk index, and it is used to quantify the risk due to intermittent nature of RERs and EVs. The main contribution of this paper lies in its ability to determine the optimal generation schedules by optimizing operating cost and risk. These two objectives are solved by using a multiobjective-based nondominated sorting genetic algorithm-II (NSGA-II) algorithm, and it is used to develop a Pareto optimal front. A best-compromised solution is obtained by using fuzzy min-max approach. The proposed approach has been implemented on modified IEEE 30 bus and practical Indian 75 bus test systems. The obtained results show the best-compromised solution between operating cost and system risk level, and the suitability of CVaR for the management of risk associated with the uncertainties due to RERs and EVs.     

Short-Term Hydroscheduling with Discrepant Objectives Using Multi-Step Progressive Optimality Algorithm1

Cheng, Chuntian, Jianjian Shen, Xinyu Wu, and Kwok-wing Chau, 2012. Short-Term Hydroscheduling with Discrepant Objectives Using Multi-step Progressive Optimality Algorithm. Journal of the American Water Resources Association (JAWRA) 48(3): 464-479. DOI: 10.1111/j.1752-1688.2011.00628.x Abstract: With increase in the number and total capacity of hydropower plants in power systems, optimality algorithms with a single objective are not suitable for optimizing the operation of complex hydropower systems to meet complex demands. Hydropower plants should prioritize discrepant objectives, such as peak regulation and maximizing generation during solving of optimal operation problems of hydropower systems. In this article, we present a multi-step progressive optimality algorithm (MSPOA) for the short-term hydroscheduling (STHS) problem to improve the quality of optimal solutions and enhance the convergence speed of progressive optimality algorithm (POA). In MSPOA, the original problem is first decomposed into a sequence of problems with the longer time steps. Next, the problem with the longest time step is solved, and the optimal solution is used as the initial solution for the problem with the second longest time step. This process proceeds until the original problem with the shortest time step is solved. The proposed discrepant-objective method and solution technique are tested for two types of hydroelectric systems. The results show that MSPOA can give better solutions and cost less time than POA due to enlarging feasible range of decision variables and reducing the number of computational stages. Discrepant objectives among hydropower plants can express the operation characteristics of complex hydropower systems more accurately than unique objective or multiple objectives.