A delivery service using unmanned aerial vehicles (UAVs) has potential as a future business opportunity, due to its speed, safety and low-environmental impact. To operate a UAV delivery network, a management system is required to optimize UAV delivery routes. Therefore, we create a routing algorithm to find optimal round-trip routes for UAVs, which deliver goods from depots to customers. Optimal routes per UAV are determined by minimizing delivery distances considering the maximum range and loading capacity of the UAV. In order to accomplish this, we propose an algorithm with four steps. First, we build a virtual network to describe the realistic environment that UAVs would encounter during operation. Second, we determine the optimal number of in-service UAVs per depot. Third, we eliminate subtours, which are infeasible routes, using flow variables part of the constraints. Fourth, we allocate UAVs to customers minimizing delivery distances from depots to customers. In this process, we allow multiple UAVs to deliver goods to one customer at the same time. Finally, we verify that our algorithm can determine the number of UAVs in service per depot, round-trip routes for UAVs, and allocate UAVs to customers to deliver at the minimum cost.

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South Korea announced an energy transition roadmap, CO2 roadmap, and national greenhouse gas reduction target of nationally determined contribution (NDC) for the Paris Agreement. Furthermore, the government has also set a goal of reducing its CO2 emissions to reach net-zero carbon emissions by 2050. Additionally, the Korean government submitted an enhanced update of the first NDC at the end of 2021. In the electricity sector, the updated NDC proposed the GHG emissions target of 149.9 million tons in 2030. In this study, we model eight scenarios based on future energy mix and demand forecast considering the government’s latest plans to evaluate the possible emission reduction and impacts in the electricity sector. The scenario-based analysis is conducted to check whether it can satisfy the CO2 reduction target by using PLEXOS, a production simulation model. The results show that emission reduction targets are difficult to accomplish in the short term and can lead to significant changes in the operation of generators and increased costs to realize the decarbonization pathway.

We synthesize scenarios of hourly electricity price, which is known as the system marginal price (SMP), for thirty-years based on the oil price. Hourly SMP scenarios are very important when planning new generators because the revenue and cost of new capacity margins are determined based on the SMP. Because the SMP contains both short-term and long-term periodic patterns, designing a single model based on these patterns to predict the SMP is difficult. Although oil price affects SMP, they can not be directly used in the forecasting model because the resolution of SMP is at hourly intervals, but that of oil price is at yearly intervals. To overcome these problems, we decompose the SMP into annual, monthly, and daily components, and the components are predicted based on different models. The model for the annual component (AC) is designed to predict the long-term trend based on fuel price scenarios. The model for the monthly component (MC) is designed to predict the seasonal trends based on the long short term memory (LSTM) model. The model for the daily component (DC) is designed to predict the daily SMP fluctuation. Finally, we synthesize SMP scenarios by aggregating three components. We make three types of SMP scenarios (high, reference, and low), and the performance of the scenarios is tested using previous data for two years on the basis of mean absolute error (MAE). Due to the global COVID-19 pandemic, the low type of SMP scenario is most accurate. We also verify that the reliability of long-term scenarios can be secured by using oil price while maintaining monthly and daily patterns. INDEX TERMS Time series analysis, Neural network, System marginal priceThe entry of new generators depends on predicted elec-1 tricity price, which is known as the system marginal price 2 (SMP). The SMP is the electricity price in electricity net-3 works without any binding constraints of transmission limits. 4 The SMP represents the most expensive variable cost of all 5 on-line generators [1]. When planning a new generator, a 6 long-term prediction of hourly SMP is indispensable because7it directly relates to their profits. However, the SMP is highly 8 volatile and affected by many factors, such as electricity 9 demand, weather, fuel prices, economic conditions, and date 10 [2]. Thus, predicting hourly SMPs over several years is 11 difficult because those factors fluctuate for several years.

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