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• Employs non-identical parallel machines in manufacturing with multiple energy sources. • Designs a bi-objective model for a high-mix, low-volume production scheduling problem. • Provides Pareto-optimal solutions that minimize both total cost and carbon emissions. • Validates a model with real-world application to a manufacturer in Ulsan, South Korea. • Analyzes the effects of price fluctuations, different combinations of energy sources. This paper addresses the production scheduling problem with non-identical parallel machines in a high-mix, low-volume production environment with due dates, considering carbon emissions savings and diverse types of energy sources for machine operation. To this end, we present a bi-objective mixed-integer programming model that minimizes both the total production-related cost and the total carbon emissions in the production process to determine the optimal production strategy. For each machine, we consider various types of energy sources with different electricity generation costs and different amounts of carbon emissions. The proposed model is validated with an application to a manufacturer in Ulsan, the industrial capital of the Republic of Korea. Our results showcase the potential use of non-identical parallel machines to minimize the total cost and carbon emissions in green manufacturing. Furthermore, the results identify the trade-off between different energy sources and the total cost under different carbon emissions limits. Generally, as the carbon emissions limit increases, our proposed model tends to replace natural gas with coal to minimize the total cost. Also, we perform sensitivity analyses with respect to the energy price of different types of energy sources combined with nuclear and renewable energy sources. We find that coal provides more stability than natural gas in terms of the total cost, particularly when there are fluctuations in the price for coal and natural gas. Additionally, we determine the optimal combination of energy sources for various carbon emissions limits, aiming to minimize the total cost while simultaneously satisfying all production-related and environmental constraints. [ABSTRACT FROM AUTHOR]

With the growing importance of renewable energy generating resources around the world, the government is working towards expanding the share of renewable energy to 20% of total power generation considering the limitation of coal-fired and nuclear generation by 2030 in South Korea. However, the growing number of wind generating resources being connected to the electrical power system presents a challenge for transmission grid planning to increase the penetration of wind power while maintaining high levels of reliability and security of the electrical power system. In this paper, we propose a probabilistic power output model of wind generating resources for network congestion management. We use the historical data from the wind farms located on Jeju Island in South Korea to fit the Weibull distribution and implement Monte Carlo simulations. The simulation results, which represent network congestion with probabilistic values, are applied to the empirically modeled power grid of Jeju Island and a steady-state security evaluation is performed. The proposed probabilistic approach will be a key role to reduce the risk of over-investment in power transmission facilities compared to the deterministic approach to develop the generation mix scenarios with high wind power penetrations. • A New probabilistic approach based on Monte Carlo Simulations is presented. • A practical process for probabilistic wind power output modeling is proposed. • Probabilistic security assessments for network congestion management are suggested. • An empirical data of Jeju Island's wind farms is considered for network congestion analysis. [ABSTRACT FROM AUTHOR]

Although research on time series prediction based on deep learning is being actively carried out in various industries, deep learning technology still has a high entry barrier for researchers who have not majored in computer science. This paper presents a tutorial on time series prediction using a deep learning-based model. The entire process of time series data prediction is presented—from data collection to evaluation of prediction results. The details of each step are shown through a case example of predicting peak electricity demand and system marginal price of Jeju Island in Korea using the 1D-CNN and BiLSTM model. In Jeju Island, the proportion of renewable energy in the total power generation capacity is increased to 67% in 2021, requiring more accurate electricity demand forecasts. Therefore, using 808 days of training data from February 2018, electricity demand and SMP for the next 21 days were predicted. To make it easier for readers to follow, the example uses only open public data, and the entire Python source code is shared via a GitHub repository. The prediction error calculated by WRMSSE showed 0.42 in electricity demand and 0.63 in SMP max. A WRMSSE value less than one means that the forecast was relatively good, that is, better than naïve forecasting. This tutorial is not limited to the energy industry but can be utilized for any application requiring time-series data prediction. This article is expected to be of great help to researchers who need to understand the process of time series prediction using deep learning and use it for application in their industry. [ABSTRACT FROM AUTHOR]

In recent years, the frequency of power demand imbalance and negative price phenomenon has risen due to the rapid expansion of renewable energy sources (RES). Because of this, a means to reduce the curtailment of RES by utilizing surplus energy is essential. This paper focuses on reducing the curtailment of wind turbines (WT) with high output intermittency and minimizing the investment cost of IES via an integrated energy system (IES). The IES operation seeks to improve the acceptability and efficiency of the RES as it supports the integration of various energies mix, such as electricity, heat, hydrogen. This paper proposes an optimal facility combination set (FCS) of IES that satisfies the requirements of ISO and IPP using Multi-Objective Optimization Programming (MOP). The case study is based on a wind farm in South Korea, set in Aewol-eup, Jeju-Island. The case study results provide the best configuration of the IES energy mix with the best economic value and efficiency while satisfying ISO and IPP perspectives. • Derive the optimal design and operation solution for integrated energy systems. • Derive the optimal solution using multi-objective optimization programming. • Propose the optimal facility combination set of the integrated energy system. • Propose the consensus point between investors and users of IES. [ABSTRACT FROM AUTHOR]

Under the rising pressure for low carbon energy transitions in Northeast Asia (NEA), interconnecting local power grids for interstate power trade allows to share reserve capacity and to redistribute intermittent renewable energy across time zones, contributing to regional sustainability. Using a linear programming model that minimizes total energy system costs to meet hourly power demand, we build a hypothetical electricity transmission model for three urban demand centers interconnecting China, South Korea, and Japan to test economic and environmental competitiveness of the trilateral power trade with carbon pricing. In the economic perspective our results indicate that interstate electricity trade enables cost savings at all levels of CO2 prices tested (0–180USD/ton of CO2 emitted) through more efficient fuel consumptions on an hourly basis at the NEA regional level. However, ambitious carbon pricing (above 90 USD/ton CO2 emitted) is still essential for trade to ensure equitable trade impacts for all participating nodes. While trade utilizes gaps in resource availabilities and technology costs across nodes in cost minimization, results show inadequate carbon pricing leads to extra thermal combustions at the local level although regional total emissions reduce. • This paper proposes a phased approach to Asia Supergrid by interconnecting the three demand centers of Northeast Asia, China North, South Korea, and Japan West, without remote renewable sources as a regional energy cooperation model to accelerate carbon phaseout. • The trilateral power trade is effective in reducing the total cost of energy system at all levels of CO2 prices tested (0–180USD/ton of CO2 emitted) compared to autarkic generation, but ambitious carbon pricing (above 90) is essential for ensuring low carbon transitions at all participating nodes. • Trade enables cost savings by increasing renewable power output in export nodesfor transmission and avoiding fuel/emission cost at import nodes. New investment cost for line construction and renewable expansion is offset. [ABSTRACT FROM AUTHOR]

• A data-driven optimization algorithm is developed for reliable PtX design. • Constraint of grid utilization prevents overuse of grid for sustainable production. • The trade-off between variations and means of performance indicators is revealed. • Results offer insights into design decision-making under renewable uncertainty. Designing a reliable power-to-X (PtX) process that uses renewable energy and grid electricity as backup power is challenging due to the uncertain nature of renewables and potential limits on grid usage. To address this challenge, this paper proposes a data-driven, reliability-based optimization approach for designing a grid-assisted PtX process and understanding how the uncertainty of time-series renewable profile affects its design and optimal sizing. This approach uses generative adversarial networks (GANs) to capture the complex patterns of renewables in temporal and spatial dimensions, rather than simply treating uncertainty as a probabilistic range as in conventional methods. The optimization considers both the mean and variance of production cost as objectives, while also taking into account the level of grid energy penetration as a probabilistic constraint through the reliability analysis. A power allocation and performance evaluation model is developed and used in conjunction with the optimization to assess uncertainty propagation through the PtX process and evaluate the impact of renewable uncertainty on process performance. A case study involving the production of methanol on Jeju Island in South Korea demonstrates how the mean and variance of production cost and other performance indicators can be balanced in the reliable design of PtX processes under renewable energy uncertainty, providing insights for decision-making in such situations. [ABSTRACT FROM AUTHOR]

This paper aims to develop the mathematical model of electricity based on renewable energy and large-scale utility (eRELU) networks to achieve a low-carbon economy. Huge petrochemical industries allocated in South Korea are considered to evaluate the proposed model by techno-economic and environmental assessment subject to Korean renewable energy policy. The suggested mathematical model consists of two parts: the deterministic model to optimize industrial-scale utility networks and the stochastic model to construct clean electricity networks using variable renewable energy coupled with energy storage systems to provide feasible quantities of renewable electricity required from the optimized utility network. The resulting model is complemented by carbon capture and storage (CCS) systems in doing so the inevitable amount of greenhouse gases from boilers in utility networks can be significantly captured. Diverse scenarios under the uncertain parameters such as facility investment/operating costs and capacity factors of renewable energy are applied to the developed model, and the results show that the best scenario-based eRELU-CCS network reduces 16% of the total costs and capture/mitigate 114 tCO 2 /d comparing to the base case. It is expected that the proposed model will play an essential role in advancing the country's energy transition. [Display omitted] • Mathematical model developed for renewable energy and large-scale utility network. • CCS technology is combined with the optimized utility network for low-carbon economy. • A case study of huge petrochemical industries in South Korea were applied. • Scenario-based techno-economic and environmental assessment was conducted. • 16% of total economic costs and 114 tCO 2 /d of GHG emissions were reduced. [ABSTRACT FROM AUTHOR]