Search

The development and spread of antibiotic resistance (AR) have become major concerns because they pose pressing public health problems worldwide, and aquatic ecosystems are recognized reservoirs for antibiotic resistance genes (ARGs) and antibiotic-resistant bacteria (ARB). We reviewed the sources, distribution, and factors influencing ARGs and ARB in surface waters, and the methods used to measure and assess the risks posed to human and ecological health. The prevalence of ARGs and ARB is largely attributed to environmental contamination from fecal matter. Therefore, the distribution of AR on both regional and seasonal scales is significantly impacted by agriculture, which is related to economic development. In risk assessments, the risk of ARGs is mainly evaluated based on their mobility, pathogen carriage, and regional distribution, while the risk assessment for ARB is primarily focused on the quantities and diversities of pathogen-associated resistant bacteria. Based on this information, we suggest seven priority research questions regarding antibiotic resistance management in water environments: control of AR dissemination, advanced monitoring technologies, integrative impacts evaluation of antibiotics on resistance mechanisms and microbial communities, quantitative microbial risk assessment for ARB and ARGs, implications of horizontal gene transfer in non-pathogenic bacteria, synergistic risks of multiple resistance elements, and identification of high-risk ARGs and ARB in aquatic ecosystems. We also advocate for the implementation of national actions that focus on source management and environmental monitoring.

Linear programs are frequently employed to optimize national energy system models, which are used to find a minimum-cost energy system. For the operation, they assume perfect forecasting of the weather and demands over the whole optimization horizon and can therefore perfectly fit the energy systems’ design and operation. Therefore, they will yield lower costs than any real energy system that only has partial forecasting available. We compare linear programming with a priority list, a heuristic operation strategy which uses no forecasting at all, in a model of a climate-neutral German energy system. We find a 28% more expensive energy system under the priority list. Optimizing the same energy system model with both strategies envelopes the cost and design of any energy system that has partial forecasting. We demonstrate this by incorporating some rudimentary forecasting into a modified priority list, which actually reduces the gap to 22%. This is thus an approach to find an upper bound for how much a linear program possibly underestimates the costs of a real energy system in Germany in regard to imperfect forecasting. We also find that the two approaches differ mainly in the dimensioning and operation of energy storage. The priority list yields 63% less batteries, 73% less thermal storage and 54% more hydrogen storage. The use of renewables and other components in the system is very similar.

Integrating renewable energy sources (RES) into island microgrids is usually done to provide a cost-effective electricity supply. The integration process is carried out by scheduling generating unit operations with a unit commitment (UC) scheme to ensure low system operating costs. This article discusses developing a UC optimization method for integrating solar photovoltaic plants in Indonesia's Eastern Sumba microgrid power system. The scope of this study is the optimization algorithm of the UC, which consists of a priority list (PL) for the UC stage and an economic dispatch (ED) that relies on a genetic algorithm (GA) to minimize total operating costs (TOC). The results show that the PL-GA algorithm performs better than the extended priority list (EPL), and combinations of genetic algorithm and Lagrange, by applying continuous problem dispatch and improved binary GA hourly dispatch to meet ramping constraints. The application of RES incentive programs, such as carbon taxes and incentives for RES generation in calculating the TOC, shows an improvement in the financial feasibility analysis of the internal rate of return (IRR) and net present value (NPV) of actual projects in Indonesia. [ABSTRACT FROM AUTHOR]

Electric utilities in many Small Island Developing States (SIDS) of the Caribbean are characterized by a nearly exclusive use of thermal generation and operate using rudimentary optimization approaches based on assumed characteristics. This is due to financial constraints which limit the resources that can be spent in collecting detailed data required for proper generation optimization. This paper presents a case study investigating the generation system parameters that such a financially constrained electric utility located in the Caribbean should invest in knowing more precisely. This task is accomplished by performing a sensitivity analysis on the cost related parameters in the Unit Commitment (UC) problem. The results of this analysis can guide the Utility on how to prioritize their investments in. The results provide insights for similar utilities in the Caribbean and other SIDS in their investment considerations.

This study proposes a novel binary optimization method for solving unit commitment (UC) problems. Developed from moth-flame optimization (MFO), the proposed method is called binary alternative MFO (BAMFO). Originally, each MFO moth is restricted to fly toward a predetermined flame (solution), causing it to be trapped in the local optimum of that flame and reducing its performance. To address these drawbacks, BAMFO represents four alternative characteristics for moths to discover better flames all over the search space. The exploration and exploitation performances of BAMFO via these characteristics are balanced by a random parameter competing with the criteria of each characteristic. A repair strategy working with the priority list is created to adjust any unit status from the BAMFO simulation to satisfy all UC constraints. Then, Lambda-iteration method is adopted to solve economic dispatch problems. The proficiency of BAMFO is verified in various scales of unit systems for short-term operational scheduling. The results show that BAMFO yields better solutions than do other methods. The Wilcoxon signed rank test was conducted to prove the predominance of BAMFO.

Constructive Heuristics for the Resource Constraint Project Scheduling Problems (RCPSP) are preferred scheduling methods when the project network broadens. Then, to generate a good schedule from these heuristics, the priority list used in the algorithm becomes crucial. This paper proposes a Cluster-Based Priority List (CB-PL) method for generating lists to improve makespans of schedules obtained from constructive heuristics. The method creates more intellectual priority lists that generate lower makespans. The approach is built and fine-tuned upon the existing relative literature. The performance of the method is measured by comparing the makespan results. The experiment for the comparison uses serial and parallel scheduling schemes with seven priority rules. Then the experiment is tested through a set of benchmark data. Finally, schedules obtained through the CB-PL showed significant makespan reductions and increases in an overall number of better solutions. [ABSTRACT FROM AUTHOR]

Integration of renewable energy generators with existing electrical power systems is an effective strategy to strengthen energy security in islands and remote areas. However, in the case of high penetration of renewable energy sources (RES), operations and economic aspects must be considered. One of the power plant integration schemes is the Unit Commitment (UC), a daily scheduling method to achieve optimization goals while still meeting various operational, environmental, and technical constraints. However, the existing UC method is a generic one, therefore it is necessary to develop an optimization method with a new approach based on improved PL-GA to manage the generation scheduling in microgrid power systems. The proposed solution method is the PL-GA Algorithm, an improvement from the previous studies, with optimization on economic dispatch using GA. This algorithm uses a simple and fast PL technique for the generation scheduling and dispatch stages to obtain the initial population. Then, an accurate and more flexible GA technique is used for the economic dispatch stage to avoid local optima to get the lowest total operating costs. This method has been simulated on the electricity system in Eastern Indonesia, a developing electricity system with intermittent RES power plants with good results. [ABSTRACT FROM AUTHOR]

The Unit Commitment Problem (UCP) is an operational research problem commonly encountered in energy management. It refers to the optimum scheduling of the generating units in a power system to efficiently meet the electricity demand. UCP comprises two interrelated sub-problems: the Unit Commitment for deciding the operating state of the units at each scheduling period and the Economic Dispatch (ED) for allocating the demand among them. Various Evolutionary Algorithms (EA) have been adopted for solving UCP, commonly assisted by the Lambda iteration method for solving the ED. In this study, an EA-based method is proposed for dealing with both sub-problems, avoiding binary variables through a simple transformation function. The method takes advantage of a repair mechanism utilizing the Priority List (PL) to steer the search towards adequate generating schedules. The impact of the cost metric chosen for creating the PL on the computational results is investigated and the use of a Plurality of PL is suggested to alleviate the biases introduced by employing constant cost metrics. Furthermore, an Elitist Mutation strategy is developed to enhance the performance of the proposed EA-based method. Simulation results on various power systems validate the beneficial effect of the proposed modifications. Compared to state of the art, the algorithm proposed has been at least equivalent, exhibiting consistently solutions of lower or competitive costs in all systems examined. [ABSTRACT FROM AUTHOR]

Horticulture is one of the main pathways of deliberate introduction of non-native plants, some of which might become invasive. Of the 914 commercial ornamental outdoor plant species sold in Spain, 700 (77%) are non-native (archaeophytes excluded) marketed species. We classified these into six different lists based on their invasion status in Spain and elsewhere, their climatic suitability in Spain and their potential environmental and socioeconomic impacts. We found sufficient information for 270 species. We provide a Priority List of eight regulated invasive species that were still available on the market. We also established an Attention List with 68 non-regulated invasive and potentially invasive species that might cause various impacts. To prioritise the species within the Attention List, we further assessed the risk of invasion of these species by using an adaptation of the Australian WRA protocol and the level of societal interest estimated from values of the Google Trends tool. We also propose a Green List of seven species with probably no potential to become invasive, a Watch List with 27 potentially invasive species with few potential impacts and an Uncertainty List with 161 species of known status but with insufficient information to include them in any of the previous lists. We did not find sufficient information for 430 (61%) of the marketed non-native plant species, which were compiled into a Data Deficient List. Our findings of prohibited species for sale highlight the need for stronger enforcement of the regulations on invasive plant species in Spain. In addition, our results highlight the need for additional information on potential impacts and climate suitability of horticultural plants being sold in Spain, as insufficient information could be found to assess the invasion risk for most species. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic multi-layer energy management for a multi-microgrid (MMG) based smart distribution network (SDN). Also, all network constraints have been regarded to evaluate the effect of the active power loss on the energy management of the entities. Besides, demand response (DR) programs have been considered in the optimization process, which is one of the features of the SDN and could be used to manage the consumed power of each individual entity. Furthermore, in order to contemplate environmental issues, emission constraints for dispatchable generators have been taken into account. In this procedure, first, each microgrid (MG) performs an energy management to determine the schedule of its units and the shortage/surplus power. Then using the received data, the SDN operator (SDNO) prepares a priority list (PL) of units capable of injecting power to the SDN based on their average cost and capacity. The preparation of the PL entices the investors for using renewable based generators. Afterwards, it executes a global energy management. In the proposed approach, scenario based stochastic optimization has been utilized to model the uncertainties of the renewable energy resources. Finally, simulations using the modified IEEE 33-bus test feeder are done to demonstrate that how the proposed method encourages each MG for deploying clean productions as their main generation units. Moreover, the executed results illustrate the changes in the schedules of the MGs in the case of considering active power loss. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm-binary glowworm swarm optimization (BGSO) to solve the unit commitment (UC) problem. After a certain quantity of initial feasible solutions is obtained by using the priority list and the decommitment of redundant unit, BGSO is applied to optimize the on/off state of the unit, and the Lambda-iteration method is adopted to solve the economic dispatch problem. In the iterative process, the solutions that do not satisfy all the constraints are adjusted by the correction method. Furthermore, different adjustment techniques such as conversion from cold start to hot start, decommitment of redundant unit, are adopted to avoid falling into local optimal solution and to keep the diversity of the feasible solutions. The proposed BGSO is tested on the power system in the range of 10–140 generating units for a 24-h scheduling period and compared to quantum-inspired evolutionary algorithm (QEA), improved binary particle swarm optimization (IBPSO) and mixed integer programming (MIP). Simulated results distinctly show that BGSO is very competent in solving the UC problem in comparison to the previously reported algorithms.

With the rapid growth of thermostatically controlled loads, active power fluctuation and peak demand growth within an autonomous micro-grid become serious problems. This paper tries to suppress power fluctuation and shave peak demand for a micro-grid through optimizing domestic electric water heaters (controllable load). In this paper, domestic electric water heater models are first built to optimize power flow within a single water heater. Subsequently, the Monte Carlo method is proposed to simulate power consumption of a cluster of domestic electric water heaters. After that, the temperature state priority list method is presented to suppress power flow and shave peak demand for a given micro-grid. Optimization results show that the proposed temperature state priority list method can reduce peak demand by 12.5%. However, it has a wider active power fluctuation range and needs a longer reaction time compared with the simplified temperature state priority list method. In addition, the optimization results show that by increasing the number of controllable loads participating in load scheduling, active power fluctuation can be reduced and the maximum active power of the given micro-grid can be cut. However, to achieve this, about 1.2% of extra electrical energy needs to be generated by the external grid. [ABSTRACT FROM AUTHOR]

Volatile organic compounds emissions from building materials can be a major pollution source in low-occupant-density spaces. Composite-style indoor air quality references, which reflect the combined effects of multiple volatile organic compounds, can be used to determine ventilation rate requirements based on building material emissions. The lowest concentration of interest concept was adopted to implement the idea. Twenty-eight building materials selected from the National Research Council of Canada database were subjected to emission modelling, resulting in 101 volatile organic compounds as a starting volatile organic compound pool. A method was proposed to generate a volatile organic compound priority list that determines ventilation rate requirements while considering ozone-initiated reactions. Three priority lists were obtained based on three lowest concentration of interest schemes, i.e., AFSSET, AgBB and EU-LCI, with each consisting of 17–21 volatile organic compounds that were most likely to attribute to large ventilation rate requirements. Also, analyses of selected volatile organic compounds showed that the changes in the composition of the priority lists due to ozone-initiated reactions could be ignored at a typical indoor ozone concentration level. The application of priority lists was discussed for source control and air cleaning device testing. This paper provides a method to prioritize the chemicals based on ventilation rate requirements with a goal of developing volatile organic compound control strategies at building design stage. [ABSTRACT FROM AUTHOR]

This paper has proposed a novel Hybrid modified Genetic – Imperialist Competitive Algorithm (HGICA) for solving thermal Unit Commitment Problem (UCP). The UCP is a mixed integer problem with many equality and inequality constraints like the minimum down and minimum up time, spinning reserve, and ramp rate so need to a complex optimization process. In this paper the constraint handling of the problem is realized without any penalizing of solutions so a wide range of feasible solutions will be available for final optimum response. The proposed modified genetic is a novel method which has better performance than its original version and helps ICA to find more optimized responses and escape for local minimum areas easily. The main advantages of HGICA are good quality of the solution and high computational speed, which make it a suitable method for solving optimization problems. This method is carried out for three case studies including 10 and 20 units systems to efficiency of it be proved. Also the obtained results is compared to other optimization methods represented in literature for different scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a methodology to solve Unit Commitment (UC) problem from a probabilistic perspective is developed and illustrated. The method presented is based on solving the Economic Dispatch (ED) problem describing the Probability Distribution Function (PDF) of the output power of thermal generators, energy not supplied, excess of electricity, Generation Cost (GC), and Spinning Reserve (SR). The obtained ED solution is combined with Priority List (PL) method in order to solve UC problem probabilistically, giving especial attention to the probability of providing a determined amount of SR at each time step. Three case studies are analysed; the first case study explains how PDF of SR can be used as a metric to decide the amount of power that should be committed; while in the second and third case studies, two systems of 10-units and 110-units are analysed in order to evaluate the quality of the obtained solution from the proposed approach. Results are thoroughly compared to those offered by a stochastic programming approach based on mixed-integer linear programming formulation, observing a difference on GCs between 1.41% and 1.43% for the 10-units system, and between 3.75% and 4.5% for the 110-units system, depending on the chosen significance level of the probabilistic analysis. [ABSTRACT FROM AUTHOR]

Antibiotics are emerging pollutants largely considered to have a lower risk based on persistent, bioaccumulative, toxic (PBT) risk assessments. However, an increasing number of studies have illustrated that antibiotics are responsible for the global increase in antimicrobial resistance (AMR), which suggests that the risk of antibiotics has been largely underestimated by using PBT risk assessment. Here, we designed an integrated innovation risk assessment framework of persistent, bioaccumulative, toxic, and resistance (PBTR) that accounts for antibiotic resistance to better represent the antibiotic environmental risk. This novel antibiotic risk assessment framework was further verified via application to 39 target antibiotics in the 23 drinking water sources of the lower Yangtze River (LYR), China, during the normal and flood seasons. In contrast with the PBT assessment, single toxicity assessment and single resistance assessment, in the PBTR assessment, 7 of 39 target antibiotics with bacterial insensitivity were observed to represent a more prominent risk, as were the sites sampled during the flood season with low concentrations but high pollution loads, which confirmed that the sensitivity of PBTR risk assessment was instructive. The PBTR risk assessment for the screened priority antibiotics contributes not only representative data but also an innovative approach for identifying resistance risks. Using the positive matrix factorization (PMF) model, the sources of priority antibiotics can be predicted and thus supported the corresponding policy. Overall, this study first constructed a PBTR risk assessment framework, then applied it to facilitate the accurate management of antibiotic pollution at the basin level. • An assessment framework of persistent, bioaccumulative, toxic and resistance （PBTR） for antibiotics was proposed. • The PBTR risk assessment contributed to updating the method for screening the priority antibiotics for resistance risks. • Antibiotics of priority could be source-predicted by PMF model, thereby providing a premise for their accurate management. [ABSTRACT FROM AUTHOR]

This paper proposes an advanced three-stage approach to solve the unit commitment problem. The proposed approach utilizes three different stages to get the optimum solution. In the first stage, a primitive structure of all units is obtained on the basis of predefined priority. In the second stage, a weight-improved crazy particle swarm optimization considering a pseudo-inspired algorithm has been proposed for economic scheduling of operating units. Finally, in the third stage, extra reserve and total operating cost are minimized using solution restructuring process. In addition, problem formulation includes multi-fuel options, prohibited operating zones and nonlinearities like valve point loading effects. The effectiveness of proposed approach is tested on various systems including IEEE 118-bus system and its performance is compared with the existing methods with the help of simulation results. [ABSTRACT FROM AUTHOR]

This paper presents an approach to solve the unit commitment problem using a newly developed Multi-agent Evolutionary Programming incorporating Priority List optimisation technique (MAEP-PL). The objective of this study is to search for generation scheduling such that the total operating cost can be minimised when subjected to a variety of constraints, while at the same time reducing its computational time. The proposed technique assimilates the concepts of Priority Listing (PL), Multi-agent System (MAS) and Evolutionary Programming (EP) as its basis. In the proposed technique, deterministic PL technique is applied to produce a population of initial solutions. The search process is refined using heuristic EP-based algorithm with multi-agent approach to produce the final solution. The developed technique is tested on ten generating units test system for a 24-h scheduling period, and the results are compared with the standard Evolutionary Programming (EP), Evolutionary Programming with Priority Listing (EP-PL) and Multi-agent Evolutionary Programming (MAEP) optimisation techniques. From the obtained results and the comparative studies, it was found that the proposed MAEP-PL optimisation technique is able to solve the unit commitment problem where the total daily generation cost is effectively minimised and the computation time is reduced as compared to other techniques. [ABSTRACT FROM AUTHOR]

For many years, the UC (unit commitment) problem has been solved by complex numerical techniques or intelligent search algorithms, due to nonlinear and complex constraints. Many of the applied algorithms employ random searches, which leads to production of different solutions in different program runs. Priority list-based methods are a way out to this, as they produce robust results during a non-iterative procedure, and without help of trial and error efforts. Nevertheless, they have all proven inefficient. This paper introduces a new approach that generates the solutions using algorithm-specific constraint handling techniques, based on the priority list concept. The solution-making stages include: 1. Minimum up/down time establishment using a probabilistic priority list-oriented selection mechanism, 2. Spinning reserve constraint handling through a deterministic priority list-based process, 3. Power balance handling and a ramp rate modification procedure for generating efficient ramp-constrained solutions. The different steps are designed such that efficient modifications are applied in each step without violating the previously established constraints. Simulation results on different test systems reveal that the approach obtains robust and competitive results. A new 140-unit large-scale test system based on Korean power system is also presented for verifying applicability of the proposed approach on real world power systems. [ABSTRACT FROM AUTHOR]

In this paper, security constrained generation scheduling (SCGS) problem for a grid incorporating thermal, wind and photovoltaic (PV) units is formulated. The formulation takes into account the stochastic nature of both wind and PV power output and imbalance charges due to mismatch between the actual and scheduled wind and PV power outputs. A hybrid technique in which the basic elements are a genetic algorithm (GA) with artificial neural network (ANN) and a priority list (PL) is used to minimize the total operating costs while satisfying all operational constraints considering both conventional and renewable energy generators. Numerical results are reported and discussed based on the simulation performed on the IEEE 24-bus reliability test system. The results demonstrate the efficiency of the proposed approach to reduce the total production cost for real time operation. Moreover, the results verified that the proposed approach can be applied to different problem dimensions and can score more favorably compared with analytical techniques. [ABSTRACT FROM AUTHOR]

The Critical Chain Scheduling and Buffer Management (CC/BM) methodology, proposed by Goldratt (Critical chain, ), introduced the concepts of feeding buffers, project buffers and resource buffers as well as the roadrunner mentality. This last concept, in which activities are started as soon as possible, was introduced in order to speed up projects by taking advantage of predecessors finishing early. Later on, the railway scheduling concept of never starting activities earlier than planned was introduced as a way to increase the stability of the project, typically at the cost of an increase in the expected project makespan. In this paper, we will indicate a realistic situation in which railway scheduling improves both the stability and the expected project makespan over roadrunner scheduling. [ABSTRACT FROM AUTHOR]

Highlights: [•] Increasing renewables reduce “residual demand” to be met by conventional power plants. [•] MILP faces computational difficulties for low load UC problems. [•] New EPL method is developed specifically for low load UC. [•] The EPL method proves to be fast, accurate and close to optimal. [ABSTRACT FROM AUTHOR]

As a result of the differences between classical large power grids and micro grids a new approach of the Unit Commitment (UC) and Economic Dispatch (ED) problem must be proposed. The high penetrations of renewable sources and distributed energy storage systems, as well as the possibility of working in a grid-connected or island mode are some of the main issues to cope with. Firstly the advantages and drawbacks of the use of the Lambda Iteration Algorithm (LIA) for solving de ED problem in a micro grid are discussed. In order to adapt the LIA to this context some modifications have been carried out. With regard to the Unit Commitment problem, a genetic algorithm with some novel specific operators has been designed. This algorithm is suitable to deal with different constraints and scenarios arising in a micro grid environment. In addition, a comparison between the different characteristics of the designed UC algorithm and the traditional Priority List (PL) method has been performed. [ABSTRACT FROM AUTHOR]

Unit commitment (UC) problem is one of the medium term decision problems in power system and it is used to determine the mix/ combination of generators to be operated or to be idle. By solving the UC problem, once the binary data is obtained the next level is to find the optimum level of dispatch of each generators in the mix. For a large generating system UC program is a large scale, non linear mixed integer programming (MIP). This paper proposes a hybrid algorithm to solve UC problem by considering equality and inequality constraints. The objective of the algorithm is to minimize the fuel cost after meeting all the possible constraints. The proposed algorithm initially solved the UC problem by modified LR method. LR method most often gives sub optimal solution, so once the problem is solved by LR and if some of the output is infeasible then by solving it by heuristic technique such as priority list method, we can modify LR. The generation output level is determined by particle swarm optimization method. The algorithm was tested in four unit eight hour systems and ten unit twenty four hour system. The simulation output shows that the developed algorithm performed well and also applicable for large systems. [ABSTRACT FROM AUTHOR]

Abstract: Short-term combined economic emission hydrothermal scheduling (CEES) is a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. In this paper, quadratic approximation based differential evolution with valuable trade off approach (QADEVT) has been developed to solve the bi-objective hydrothermal scheduling problem. The practical hydrothermal system possesses various constraints which make the problem of finding global optimum difficult. In this paper, heuristic rules are proposed to handle the water dynamic balance constraints and heuristic strategies based on priority list are employed to handle active power balance constraints. A feasibility-based selection technique is also introduced to satisfy the reservoir storage volumes constraints. To demonstrate the superiority of the proposed approach, simulation results have been compared with those obtained by differential evolution (DE) and particle swarm optimization (PSO) with same heuristic strategies and the earlier reported methods available in literature. The simulation results reveal that the proposed approach is capable of efficiently providing superior solutions. [Copyright &y& Elsevier]

In this paper, we are proposing a novel approach for solving power economic dispatch among generation units, considering valve point effects and CO2 emissions. Here we are using particle swarm optimization (PSO) technique for solving an optimization problem. Besides a new hybrid priority list is applied for handling emissions constraint. In this respect, power economic dispatch is introduced, which comprises both objective functions and constraints. Then, the proposed algorithm is completely introduced. Efficiency of the algorithm has been tested in two system tests, as case studies. A few scenarios have been considered and simulations were conducted for demonstrating the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced PSO (EPSO) approach to solve the unit commitment (UC) problem in electric power system, which is an integrated improved discrete binary particle swarm optimization (DBPSO) with the Lambda-iteration method. The EPSO is enhanced by priority list based on the unit characteristics and heuristic search strategies to repair the spinning reserve and minimum up/down time constraints. The implementation of EPSO for UC problem consists of three stages. First, the DBPSO based on priority list is applied for unit scheduling when neglecting the minimum up/down time constraints. Second, heuristic search strategies are used to handle the minimum up/down time constraints and decommit excess spinning reserve units. Finally, Lambda-iteration method is adopted to solve economic load dispatch based on the obtained unit schedule. To verify the advantages of the EPSO method, the EPSO is tested and compared to the other methods on the systems with the number of units in the range of 10 to 100. Numerical results demonstrate that the EPSO is superior to other methods reported in the literature in terms of lower production cost and shorter computational time. [ABSTRACT FROM AUTHOR]

Festucas.l. is a crucial group of Crop Wild Relatives (CWR) genera, globally employed as fodder, lawn grasses and for habitat restoration. Lists elaborated strictly on the basis of a taxonomic approach are urgently needed to be used to find gaps in existingex situcollections, to assess the conservation status of taxa of ascertained utility and include them in national Red Lists and to identify the most valuable taxa to be included in “preservation mixtures”, as per Commission Directive 2010/60/EU. As an example, the first taxonomically based priority list of Italian CWRs ofFestucas.l. is here provided. [ABSTRACT FROM PUBLISHER]

Pharmaceuticals are substances designed to have a biological effect in humans. Their presence in the environment, especially in surface waters, is of increasing concern because of their potential risk to non-target species. A large number of pharmaceuticals are on the market; for example, approximately 2,000 active ingredients are approved in Europe, and many of them have already been detected in surface water. It is therefore crucial to select the substances that may do the most harm to the environment prior to performing measurements and extensive risk assessment. In the present study, a method to determine a list of pharmaceuticals to survey in surface water is proposed. Inclusion of substances on the list was based on a screening procedure, the analytical feasibility, and previous knowledge of pharmaceuticals detected in water. The screening procedure proposed here is an improvement on the standard procedure of the European Medicine Evaluation Agency (EMEA). It is designed to decrease the number of pharmaceuticals to be evaluated in a stepwise manner, thus decreasing the number of data necessary for the evaluation. We applied our approach to determine a list of 37 pharmaceuticals and four hormones to survey in a specific region of Switzerland, the Lake Geneva area, and discussed the advantages and weak points of the method. Environ. Toxicol. Chem. 2010; 29:1649–1657. © 2010 SETAC [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms. [Copyright &y& Elsevier]

The European Parliament, and the Council Regulation on Medicinal Products for Paediatric Use, intend to increase children's health in Europe by promoting research, development and authorisation of medicines for paediatric use. The main objectives are: to increase the development of medicine for children to ensure that paediatric medicines are subject to high quality research to ensure that medicines for children are appropriately authorised for paediatric use to improve information available on the use fo medicines for children to avoid unnecessary studies in children. [ABSTRACT FROM AUTHOR]

Abstract: The occurrence of polycyclic aromatic hydrocarbons (PAHs) in the marine environment has attracted the attention of the scientific community, as these compounds are frequently detected in seawater and sediments at increasing levels and can have adverse health effects on marine organisms and humans. Several PAHs are potential human carcinogens and are included in the priority list of the European Union’s Water Framework Directive (2000/60/EC). Research regarding their environmental levels requires their determination by gas-chromatography and liquid-chromatography techniques, which have been developed and optimized, especially for marine-sediment samples. Results of sample analyses reveal the increasing occurrence of many species of PAHs worldwide, especially in marine sediments, where they finally accumulate, mostly in areas near intense industrial activities. In parallel, research on the toxicity of PAHs and their mixtures is continuing and is aiming to provide more insight into the health risks associated with the levels of PAHs in the environment. [Copyright &y& Elsevier]

Abstract: This paper proposes a new improved binary PSO (IBPSO) method to solve the unit commitment (UC) problem, which is integrated binary particle swarm optimization (BPSO) with lambda-iteration method. The IBPSO is improved by priority list based on the unit characteristics and heuristic search strategies to repair the spinning reserve and minimum up/down time constraints. To verify the advantages of the IBPSO method, the IBPSO is tested and compared to the other methods on the systems with the number of units in the range of 10–100. Numerical results demonstrate that the IBPSO is superior to other methods reported in the literature in terms of lower production cost and shorter computational time. [Copyright &y& Elsevier]

Abstract: This paper proposes the utilization of a new sensitivity index to organize a priority list for the solution of the unit commitment problem. The discrete nature of the operational decisions is mitigated through a continuous function. The new index is derived from Lagrangian multipliers and the nonlinear optimization problem is solved using optimum power flow (OPF) formulation based on the primal-dual interior point method. The priority list is obtained as a by-product of the Lagrangian multipliers associated with the ON–OFF decision. The efficiency of the proposed methodology is demonstrated through several tests. [Copyright &y& Elsevier]

This paper presents a determination methodology for finding optimal operation schedules of thermal units (namely unit commitment) integrated with an energy storage system (ESS) to minimize total operating costs. A generic ESS formulation along with a method for solving unit commitment (UC) of thermal units with ESS is proposed to serve this purpose. The problem of unit commitment with an ESS is solved using the Priority List method. Intelligent Genetic algorithm (GA) is included in the algorithm for generating new and potential solutions. The proposed method consists of two steps. The first step is to determine the schedule of ESS and the schedule of thermal units. The second step is to dispatch the hourly output of thermal units and the ESS which comply a minimized total production cost. The proposed method is applied to a power system with ten thermal units and a large ESS. The presented simulation results show that the schedule of thermal units with an ESS of a particular life cycle, achieved by the proposed method, minimizes the operating cost. The discussion regarding the determination of schedule thermal units (TU) along with the integrated ESS may interest many types of ESS due to their generalized formulations. [ABSTRACT FROM AUTHOR]