DERIVING THE NONLINEAR RISK-BENEFIT ALGORITHM FOR RESERVOIRS1

ABSTRACT: The Nonlinear Risk-Benefit (NRB) Algorithm includes risk as one of the objectives in a multiple-objective optimization problem. The NRB Algorithm is derived by extending the Surrogate Worth Trade-Off method to quadratic programming. This category of problem is common in water resources planning and design, especially multipurpose reservoir systems. Consequently, an example is given using the algorithm for optimally operating a multipurpose reservoir.     

MULTIOBJECTWE OPTIMIZATION OF RESERVOIR SYSTEMS OPERATION1

ABSTRACT: Development of optimal operational policies for large-scale reservoir systems is often complicated by a multiplicity of conflicting project uses and purposes. A wide range of multiobjective optimization methods are available for appraising tradeoffs between conificting objectives. The purpose of this study is to provide guidance as to those methods which are best suited to dealing with the challenging large-scale, nonlinear, dynamic, and stochastic characteristics of multireservoir system operations. As a case study, the selected methodologies are applied to the Han River Reservoir System in Korea for four principal project objectives: water supply and low flow augmentation; annual hydropower production, reliable energy generation, and minimization of risk of violating firm water supply requirements. Additional objectives such as flood control are also considered, but are imposed as fixed constraints.     

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.     

A MULTIOBJECTIVE MODEL FOR REGIONAL WATER QUALITY MANAGEMENT1

ABSTRACT: Linear programming is the simplest of all the optimization techniques used in regional water quality management studies; but the technique can optimize only one goal. When there are multiple goals with the same or different priorities, goal programming is a useful decisionmaking tool. This paper illustrates the application of goal programming to a regional water quality management problem where the following two goals are considered: (1) minimize the total cost of waste treatment, and (2) maintain the water quality goals (dissolved oxygen) close to the minimum level stated in the stream standards.     

Using many-objective trade-off analysis to help dams promote economic development,protect the poor and enhance ecological health

Allocating water to different uses implies trading off the benefits perceived by different sectors. This paper demonstrates how visualising the trade-offs implied by the best performing water management options helps balance water use benefits and find sustainable solutions. The approach consists of linking a water resources model that can simulate many management policies and track diverse measures of system performance, to a many-objective evolutionary optimisation algorithm. This generates the set of Pareto-optimal management alternatives for several simultaneous objectives. The relative performance of these efficient management alternatives is then visualised as trade-off curves or surfaces using visual analytic plots. Visually assessing trade-offs between benefits helps select policies that achieve a decision-maker-selected balance between different metrics of system performance. We apply this approach to a multi-reservoir water resource system in Brazil's semi-arid Jaguaribe basin where current water allocation procedures favour sectors with greater political power and technical knowledge. The case study identifies promising reservoir operating policies by exploring trade-offs between economic, ecological and livelihood benefits as well as traditional hydropower generation, irrigation and water supply. Results show optimised policies can increase allocations to downstream uses while increasing median land availability for the poorest farmers by 25%.     

Benefits of integrating complementarity into priority threat management

Conservation decision tools based on cost‐effectiveness analysis are used to assess threat management strategies for improving species persistence. These approaches rank alternative strategies by their benefit to cost ratio but may fail to identify the optimal sets of strategies to implement under limited budgets because they do not account for redundancies. We devised a multiobjective optimization approach in which the complementarity principle is applied to identify the sets of threat management strategies that protect the most species for any budget. We used our approach to prioritize threat management strategies for 53 species of conservation concern in the Pilbara, Australia. We followed a structured elicitation approach to collect information on the benefits and costs of implementing 17 different conservation strategies during a 3‐day workshop with 49 stakeholders and experts in the biodiversity, conservation, and management of the Pilbara. We compared the performance of our complementarity priority threat management approach with a current cost‐effectiveness ranking approach. A complementary set of 3 strategies: domestic herbivore management, fire management and research, and sanctuaries provided all species with >50% chance of persistence for $4.7 million/year over 20 years. Achieving the same result cost almost twice as much ($9.71 million/year) when strategies were selected by their cost‐effectiveness ranks alone. Our results show that complementarity of management benefits has the potential to double the impact of priority threat management approaches.     

Increasing the microbial activity in activated sludge by the phenomenon of 'biological resonance

Mechanical oscillations as well as electrical oscillations (e.g. in an electrical oscillatory circuit) can lead to resonance conditions by external agitation with the natural frequency of the system. During the microbial protein synthesis, the induction and repression mechanisms also cause oscillations in the form of varying protein concentrations. The aim of this work concentrates on an induced resonance case of the biological system, comparable with physical resonance phenomena, by external periodic stimulations. For this, system theoretical computer simulations with a structured genetic model were carried out, which built the theoretical fundament for describing the effect of 'Biological Resonance'. Based on that, lab-scale experiments using a mixed microbial culture (activated sludge) and skimmed milk as an inducing substrate revealed an enhanced microbial productivity of around 60%-75% compared to values of the productivity under steady-state conditions in a narrow range of process parameters. The optimum (resonance case) was confirmed by a repetition of the variation experiments and is characterised by a substrate supply period of approx. 18 min and a following starvation period of about 9 min. Long-term investigations under optimised process conditions indicate adaptation mechanisms of the microorganisms to evade the imposed stress conditions. The productivity will not remain constant without additional stimulation and declines to its original level. However, temporal modulation of the starvation period permanently increases the productivity (elevated catabolism) to about 60%, which was observed over a period of several weeks.     

污泥水磷和有机物同步混凝去除的多目标优化技术研究

通过引入多响应值的归一化评分法进行了污泥水磷和有机物同步混凝去除的多目标优化,并利用响应面(RSM)技术考察了Al/P比、聚丙烯酰胺(PAM)投加量和悬浮固体(SS)浓度对污染物去除的单独效应和联合效应.结果表明,复合投加聚合氯化铝和PAM能同步去除污泥水中磷和有机物,并改善沉降效果.归一化后单目标RSM优化显示,对污泥水中磷和有机物同步去除的贡献为Al/P比SS浓度PAM投加量.在最优条件Al/P比为3、PAM浓度为1.22 mg·L~(-1)、SS浓度为3.58 g·~(L-1)的条件下,正磷和总有机碳去除率分别为93.1%和53.9%.与多响应变量优化相比,引入归一化评分法有效解决了变量间数值量级差异的问题,使结果的分析计算变得简单方便.     

Simultaneous copper bioaccumulation,growth and lipase production of Rhizopus delemar in molasses medium: optimisation of environmental conditions using RSM

In this study, the bioaccumulation of Cu(II) ions by R. delemar and their influence on growth of the microorganism were assessed, and the impact of Cu(II) ions on the activity of lipase enzyme degradation of lipids in wastewaters by enzymatic hydrolysis was received for consideration. Optimisation of Cu(II) bioaccumulation process was performed by varying three independent parameters (pH, temperature and Cu(II) concentration) using a central composite design (CCD) under response surface methodology (RSM). For the maximum Cu(II) bioaccumulation and lipase production at a desired level of the biomass concentration level, a total of 20 experimental runs were set and the experimental data fitted to the empirical second-order polynomial models. The adequacies of the predicted models for the Cu(II) bioaccumulation, lipase activity and biomass concentration were confirmed by the coefficients of multiple regression (R²), which were adjusted to be 99.6%, 94.5%, 93.4%, respectively indicating reasonably good models for practical implementation. A maximum Cu(II) bioaccumulation of 36.81 mg/L was obtained at pH 6.0, at 32.6°C, and at 50 mg/L initial Cu(II) concentration. The lipase activity of R. delemar decreased only from 505 U/L in Cu(II)-free fermentation medium to 476 U/L in the presence of Cu(II) giving 5.74% decrease in lipase activity.     

The specification of sustainable urban transport strategies

Transport strategies have been developed for nine European cities to achieve optimum performance in terms of a range of objective functions. The functions selected represent economic efficiency, sustainability and a combination of these. The strategies have been based on combinations of a standard set of policy instruments, including public transport infrastructure, frequency and fares, road capacity increases, low cost road pricing and parking charges. The optimisation method is described, and results presented. The reactions of city authorities to the proposed strategies are discussed and implications for transport policy outlined.