(2000). DETERMINATION OF THE OPTIMAL PROCESS MEAN WITH THE CONSIDERATION OF VARIANCE REDUCTION AND PROCESS CAPABILITY. Quality Engineering: Vol. 13, No. 2, pp. 251-260.

Multi-objective power scheduling (MOPS) aims to address the simultaneous minimization of economic costs and different types of environmental emissions during electricity generation. Recognizing it as an NP-hard problem, this article proposes a novel multi-agent deep reinforcement learning (MADRL)-based optimization algorithm. Within a custom multi-agent simulation environment, representing power-generating units as collaborative types of reinforcement learning (RL) agents, the MOPS problem is decomposed into sequential Markov decision processes (MDPs). The MDPs are then utilized for training an MADRL model, which subsequently offers the optimal solution to the optimization problem. The practical viability of the proposed method is evaluated across several experimental test systems consisting of up to 100 units featuring bi-objective and tri-objective problems. The results demonstrate that the proposed MADRL algorithm has better performance compared to established methods, such as teaching learning-based optimization (TLBO), real coded grey wolf optimization (RCGWO), evolutionary algorithm based on decomposition (EAD), non-dominated sorting algorithm II (NSGA-II), and non-dominated sorting algorithm III (NSGA-III).

The transportation sector is recognized as one of the largest contributors to the problems of global warming and environmental pollution, and is responsible for a great deal of global energy consumption, which is heavily dependent upon scarce crude oil reserves. Different countries have adopted promotional policies to replace conventional internal combustion engine vehicles with electric vehicles as a means of mitigating global warming. Nevertheless, the current market share of eco-friendly vehicles remains stagnant in many parts of the world. This study aims to investigate the impact and relative importance of financial, technical, and political measures on the market penetration of electric vehicles using an agent-based simulation. More specifically, a series of agent-based simulation experiments is carried out following the statistical experimental design scheme to systematically assess the diffusion of electric vehicles. Affected by various factors and measures, the choice behavior of individual agents is modeled with a multinomial logit utility function of experimental factors. The simulated data are analyzed using different analysis methods, including full factorial analysis, response surface methodology, and support vector machine, in order to scrutinize the effects of different measures. It is advocated that factors affecting the choice of vehicle by individuals, including two-way interactions among various measures as well as policy measures such as purchase subsidies and tax breaks, have more significant effects on the widespread adoption of electric vehicles than do technical improvements in terms of battery charging times and driving mileage. This implies that the adoption of such measures needs to be carefully designed in order to account for potential interactions among individual measures as well as their main effects on the diffusion of electric vehicles.

The estimation of power curve is the central task for efficient operation and prediction of wind power generation. It is often the case, however, that the actual data exhibit a great deal of variations in power output with respect to wind speed, and thus the power curve estimation necessitates the detection and proper treatment of outliers. This study proposes a novel procedure for outlier detection and elimination for estimating power curves of wind farms by employing clustering algorithms of vector quantization and density-based spatial clustering of applications with noise. Testing different parametric models of power output curve, the proposed methodology is demonstrated for obtaining power curves of individual wind turbines in a Korean wind farm. It is asserted that the outlier elimination procedure for power curve modeling outlined in this study can be highly efficient at the presence of noises.

The widespread outbreak of the novel coronavirus disease COVID-19 has posed an enormous threat to global public health. A different set of policy interventions has been implemented to mitigate the spread in most countries. While the use of personal protective equipment and social distancing has been specifically emphasized, South Korea has deployed massive testing and contact-tracing program from the early stage of the outbreak. This study aims at investigating the effectiveness of testing and contact-tracing to counter the spread of infectious diseases. Based on the SEICR (susceptible-exposed-infectious-confirmed-recovered) model, an agent-based simulation model is developed to represent the behavior of disease spreading with the consideration of testing and contact-tracing in place. Designed experiments are conducted to verify the effects of testing and contact tracing on the peak number of infections. It has been observed that testing combined with contact tracing may lower the peak infections to a great extent, and it can thus be avoided for the hospital bed capacity to be overwhelmed by infected patients. It is implied that an adequate capability of testing and contact-tracing may enable us to become better prepared for an impending risk of infectious diseases.

The waste sector may play a significant role in national mitigation policies with further greenhouse gas (GHG) reduction opportunities mainly because of its linkage to other sectors. However, the waste sector has not drawn much attention from research community mainly because the amount of GHG emissions from the waste sector is notably smaller than other sectors. This study presents emissions estimation and mitigation potentials of the waste sector in Korea. Emission estimates and business-as-usual emissions through 2050 are estimated based on four different treatment methods, including landfill, incineration, wastewater, and biological treatment by considering country-specific emission parameters of wastes, where available. Different types of wastes for each treatment method are investigated to obtain accurate emission estimates. It is expected that GHG emissions in 2050 are about 12.0 Tg CO₂eq, which is 17% less than those in 2010. Mitigation potentials and economic impacts of five different measures are also investigated, and it is revealed that the production of refuse drive fuel from combustible municipal solid wastes may render the greatest benefit with the most mitigation potential of 649 ktCO₂eq. An interdependent nature among mitigation measures is further discussed and it is shown that, if implemented together, the accumulated mitigation potentials are far less than the simple sum of individual potentials. It is implied that an aggregate potential of individual measures needs to be examined when implementing several mitigation measures simultaneously. This study outlines how to investigate emissions estimation and mitigation pathways for the waste sector in a national level.