In this paper, the impact of the wind power generation system on the total cost and profit of the system is studied by using the proposed procedure of binary Sine Cosine (BSC) optimization algorithm with optimal prior...In this paper, the impact of the wind power generation system on the total cost and profit of the system is studied by using the proposed procedure of binary Sine Cosine (BSC) optimization algorithm with optimal priority list (OPL) algorithm. As well, investigate the advantages of system transformation from a regulated system to a deregulated system and the difference in the objective functions of the two systems. The suggested procedure is carried out in two parallel algorithms;The goal of the first algorithm is to reduce the space of searching by using OPL, while the second algorithm adjusts BSC to get the optimal economic dispatch with minimum operation cost of the unit commitment (UCP) problem in the regulated system. But, in the deregulated system, the second algorithm adopts the BSC technique to find the optimal solution to the profit-based unit commitment problem (PBUCP), through the fast of researching the BSC technique. The proposed procedure is applied to IEEE 10-unit test system integrated with the wind generator system. While the second is an actual system in the Egyptian site at Hurghada. The results of this algorithm are compared with previous literature to illustrate the efficiency and capability of this algorithm. Based on the results obtained in the regulated system, the suggested procedure gives better results than the algorithm in previous literature, saves computational efforts, and increases the efficiency of the output power of each unit in the system and lowers the price of kWh. Besides, in the deregulated system the profit is high and the system is more reliable.展开更多
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 ...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.展开更多
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 th...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 quantuminspired 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.展开更多
文摘In this paper, the impact of the wind power generation system on the total cost and profit of the system is studied by using the proposed procedure of binary Sine Cosine (BSC) optimization algorithm with optimal priority list (OPL) algorithm. As well, investigate the advantages of system transformation from a regulated system to a deregulated system and the difference in the objective functions of the two systems. The suggested procedure is carried out in two parallel algorithms;The goal of the first algorithm is to reduce the space of searching by using OPL, while the second algorithm adjusts BSC to get the optimal economic dispatch with minimum operation cost of the unit commitment (UCP) problem in the regulated system. But, in the deregulated system, the second algorithm adopts the BSC technique to find the optimal solution to the profit-based unit commitment problem (PBUCP), through the fast of researching the BSC technique. The proposed procedure is applied to IEEE 10-unit test system integrated with the wind generator system. While the second is an actual system in the Egyptian site at Hurghada. The results of this algorithm are compared with previous literature to illustrate the efficiency and capability of this algorithm. Based on the results obtained in the regulated system, the suggested procedure gives better results than the algorithm in previous literature, saves computational efforts, and increases the efficiency of the output power of each unit in the system and lowers the price of kWh. Besides, in the deregulated system the profit is high and the system is more reliable.
基金financially supported by the National Key Research and Development Program of China(2021YFC3200100).
文摘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.
文摘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 quantuminspired 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.
