APV systems producing both crops and electricity are becoming popular as an alternative way of producing renewable energy in many countries with land shortage issues (e.g., South Korea). This study aims at developing a hybrid performance model of an Agrophotovoltaic (APV) system that produces crops underneath the PV modules. In this study, the physical model used to estimate solar radiation is integrated with a polynomial regression approach to forecast the amount of electricity generation and crop production in the APV system. The model takes into account not only the environmental factors (i.e., daily temperature, precipitation, humidity, and wind speed) but also physical factors (i.e., shading ratio of the APV system) related to the performance of the APV system. For more accurate modelling, the proposed approach is validated based on field experiment data collected from the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. As a result, the proposed approach can predict the electricity generation quantity in the APV system with an R² of 80.4%. This will contribute to the distribution of the APV system, which will increase farmers’ income as well as the sustainability of our society.

Optimization is a key management science methodology utilizing mathematical techniques to determine optimal solutions to a variety of management challenges. The biofuel production process, comparable to existing supply chain operations, consists of complex interconnected activities among three principal components: farms, distribution networks, and refineries. To effectively manage the complex and large-scale biofuel supply chain network, it is essential to employ optimization methodologies such as linear programming and nonlinear programming. However, existing optimization methods are predominantly systematized for generalized issues such as manufacturing production scheduling and supply chain operations management, thus a systematic guideline indicating which techniques should be employed for specific problems in biofuel production and supply relative to the production and management of new and renewable energy sources is absent. Given the crucial need for a continuous increase in biofuel production and efficient management, optimization methods should be implemented. Accordingly, this study compiles optimization techniques suitable for biofuel supply chain operations through a thorough literature review. Particularly, this study examines methods ranging from conventional linear and nonlinear programming to recently utilized simulation-based optimization techniques, spurred by advancements in computing performance. Consequently, researchers and engineers will be equipped to select and implement suitable optimization methods for various challenges in the biofuel production process.

Agrophotovoltaic (APV) systems produce both solar energy and crops, so they are considered a sustainable alternative to traditional solar power plants, which can potentially destroy farmlands. However, it is challenging to diffuse APV systems because of their high installation and operating costs. Thus, to resolve the issue by maximizing the productivity and profits of an APV system, this study aims to propose a mobile-phone-based decision support system (DSS) for a supply chain network design for APV systems in South Korea using satellite imagery incorporating geographic information system (GIS) data. Particularly, polynomial regression models estimating annual corn (Zea mays) yields and the predicted generation of electricity were developed and integrated with the proposed DSS. Field experiment data provided by the APV system at Jeollanamdo Agricultural Research and Extension Services in South Korea were utilized. Two photovoltaic (PV) module types (mono-facial and bi-facial) and three different shading ratios for APV systems (21.3%, 25.6%, and 32.0%) were considered design factors for APV systems. An optimal network structure of 6 candidate APV systems and 15 agricultural markets was devised using the generalized reduced gradient (GRG) method. The profits of the six candidate APV systems are mainly affected by the transportation costs to the markets and the policy of the electricity selling prices. As a result, the proposed supply chain design framework successfully identifies an APV system network with maximum profits from crop production as well as electricity generation.

This research proposes a novel location allocation framework that utilizes agent-based simulation for the efficient production of corn stover-based bioethanol, which requires dedicated pretreatment facilities for the feedstock. The framework comprises two main modules: (1) a Pretreatment Facility Module that assesses the performance of the corn stover-based bioethanol supply chain based on the interactions among three types of agents, namely order agent, pretreatment agent, and transport agent, and (2) an Optimization Module designed to determine the optimal supply chain configuration by selecting the most suitable number and locations for pretreatment facilities to achieve the lowest total operational cost. The framework is implemented in a case study for South Korea, which aims to raise the bioethanol blending ratio from 4% in 2025 to 8% by 2030. Experimental results reveal that, within the bioethanol supply chain comprising eight farms and four refineries, a 1% increase in bioethanol blending ratio leads to an increase in the demand for approximately 2229 kL of ethanol (10,225 tons of corn stover), and the proposed framework enables to identify the optimal location of pretreatment facilities in the subject supply chain according to the change in ethanol demand.

As renewable energy production grows, the photovoltaic (PV) module manufacturing process has received worldwide attention. In 2019, the total sales of PV modules were 1.7 billion U.S. dollars, and 78.7% of PV modules were made in South Korea. However, Korean manufacturers are facing high production costs due to high domestic labor costs and long-distance raw material procurement, making it difficult to produce price-competitive PV modules. In this situation, the best alternative for Korean manufacturers to gain a competitive edge is to produce high-quality PV modules. To this end, this study is going to propose a novel data-driven machine vision framework for the quality management of a PV manufacturing process consisting of seven stages, including tabbing, auto bussing, electro luminescence (EL), laminating, fame station, frame, and junction box. Particularly, the framework uses machine vision to analyze image data collected from an actual PV module manufacturing facility in South Korea. Autonomous decision-making algorithms are devised to recognize incorrect patterns of PV modules in terms of product quality. This experiment shows that the proposed framework enables the detection of PV module defects in electroluminescence (EL) and tabbing operations with a fault detection accuracy of over 95%. Therefore, the proposed framework enables a reduction in the number of defects, and this helps to improve quality loss during the PV module manufacturing process.

Onion (Allium cepa) is a major field vegetable in South Korea and has been produced for a long time along with cabbage, radish, garlic, and dried peppers. However, as field vegetables, including onions, have recently been imported at low prices, the profitability of onion production in South Korea is beginning to be at risk. In order to maximize farmers’ profits through onion production, this study develops onion yield prediction models via an ensemble learning-based framework involving linear regression, polynomial regression, support vector regression, decision tree, ridge regression, and lasso regression. The use of nitrogen fertilizers is considered an independent variable in the development of the yield prediction model. This is because the use of nitrogen fertilizers accounts for the highest production cost (13.47%) after labor cost (41.21%) and seed cost (17.42%), and it also directly affects onions yields. For the model development, five research datasets on changes in onion yield according to changes in the use of existing nitrogen fertilizers were used. In addition, a non-linear optimization model was devised using onion yield prediction models for the profit maximization of onion production. As a result, the developed non-linear optimization model using polynomial regression enables an increase in profits from onion production by 67.28%.