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In contrast to city-level and larger aggregate-level load forecasting, load forecasting for residential customers is a much more challenging problem because residential loads are much more volatile. In order to forecast the residential load at one-hour interval 24-h loads the day before, a 2-Step SARIMAX method for residential load forecasting is proposed in this study. The forecasting performance of the proposed method is compared with the existing forecasting methods including SARIMA.

The energy consumption of DC urban subway transit (UST) has attracted an increasing amount of attention, and various studies have focused on improving the energy use of USTs by increasing the energy efficiency or reducing the energy consumption. However, the amount of regenerative energy that can be re-utilized and the capacity of the regenerative device for re-utilization have not been extensively discussed. To simply compute the specification of the sustainability improvement facilities for a parallel-fed UST system, this paper proposes current distribution factor (CDF) based analysis that reflects the mobility and power variation of the vehicle loads in UST system. The CDF set are derived based on the electrical distance between the vehicle load and each substation, and the powerflow method with the CDF set provides an intuitive solution to analyze supplied or regenerative power flow in the parallel-fed DC UST system. Based on the actual UST line data, the convergence characteristic of the proposed algorithm was verified and the capacity of the regenerative device was calculated for peak and non-peak headway conditions.

Solar power for clean energy is an important asset that will drive the future of sustainable energy generation. As interest in sustainable energy increases with Korea’s renewable energy expansion plan, a strategy for photovoltaic investment (PV) is important from an investor’s point of view. Previous research primarily focused on assessing and analyzing the impact of the volatility but paid little attention to the modeling decision-making project to obtain the optimal investment timing. This paper utilizes a Least Squares Monte Carlo-based method for determining the timing of PV plant investment. The proposed PV decision-making method is designed to simulate the total PV generation revenue period with all uncertain PV price factors handled before determining the optimal investment time. The numerical studies with nine different scenarios considering system marginal price (SMP) and renewable energy certificate (REC) spot market price in Korea demonstrated how to determine the optimal investment time for different PV capacities. Therefore, the proposed method can be used as a decision-making tool to provide PV investors with information on the best time to invest in the renewable energy market.

This paper examines how economic growth and renewable energy consumption are associated with air pollution using a dynamic panel approach. Focusing on several major air pollutants, namely, particulate matter, sulfur dioxide, nitrogen oxide, and carbon monoxide, this paper tests the environmental Kuznets hypothesis and determines whether the use of renewable energy sources contributes to a reduction in air pollution. Data from a balanced panel of 145 countries for the period between 2000 and 2014 was used for the estimation of the dynamic panel model. The results of the dynamic panel model showed inverted U-shaped curves for the relationship between economic development and particulate matter and sulfur dioxide emissions. The results also revealed that increasing renewable energy consumption contributes to an improvement in air quality. Moreover, it was found that urbanization tends to decrease sulfur dioxide and nitrogen oxide emissions, while trade openness reduces particulate matter and carbon monoxide emissions but increases sulfur dioxide emissions.

The recent increase in the number of passengers has led to an increase in the operational costs of urban railway systems. In particular, peak demand is notably increasing due to the high affluence of commuters during rush hour. In addition, the expansion of the renewable energies has led to the adoption of sources such as photovoltaic (PV) power generation into the urban railway system. Similarly, energy storage systems (ESSs) have been introduced to reduce the peak demand. However, given the current high costs of ESSs, it is essential to accurately determine their capacity considering peak-demand reduction target in urban railway systems. In this paper, we propose an ESS sizing method considering PV power generation for peak-demand reduction in an urban railway system. The ESS capacity is determined using binary search and a linear programming based daily ESS scheduling. A numerical example considering data from urban railway and PV power generation systems shows the suitable performance of the proposed method.

Increasing renewable energy penetration rate in a power grid leads to an increase in the variability of the generated energy, which increases the system integration cost. To handle the output variations in the generation, it is necessary to secure sufficient flexible resources, such as energy storage units. Flexible resources can adjust the output quickly, which helps to increase the system flexibility. However, the electricity generation cost of the flexible resources is usually high. Because the renewable energy expansion policy is being implemented worldwide, it is necessary to evaluate the ability to manage the short-term variations of the renewable energy outputs to obtain a cost-effective long-term plan. In this study, the variability of renewable energy in Korea over the past five years was analyzed. Additionally, the backup capacity is determined to manage the variability of renewable energy output. The backup capacity is affected by system flexibility. In general, increasing system flexibility decreases the backup capacity and increases the total electricity production cost. In this study, a backup capacity planning method is proposed considering the short-term variability of renewable energy output and flexibility deficit in a power system. The numerical results illustrated the effectiveness of the proposed backup capacity planning method.

In this paper, a systematic method is proposed to cluster the energy consumption patterns of residential customers by utilizing extreme points and demographic characteristics. The extreme points of the energy consumption pattern enable effective clustering of residential customers. Additionally, demographic characteristics can be used to determine an effective extreme point for the clustering algorithm. The K-means-based features selection method is used to classify energy consumption patterns of residential customers into six types. Furthermore, the type of energy consumption pattern can be identified depending on the characteristics of residential customers. The analytical results of this paper show that the extreme points are effective in clustering the energy consumption patterns of residential customers.

Since January 2020, the COVID-19 pandemic has been impacting various aspects of people’s daily lives and the economy. The first case of COVID-19 in South Korea was identified on 20 January 2020. The Korean government implemented the first social distancing measures in the first week of March 2020. As a result, energy consumption in the industrial, commercial and educational sectors decreased. On the other hand, residential energy consumption increased as telecommuting work and remote online classes were encouraged. However, the impact of social distancing on residential energy consumption in Korea has not been systematically analyzed. This study attempts to analyze the impact of social distancing implemented as a result of COVID-19 on residential energy consumption with time-varying reproduction numbers of COVID-19. A two-way fixed effect model and demographic characteristics are used to account for the heterogeneity. The changes in household energy consumption by load shape group are also analyzed with the household energy consumption model. There some are key results of COVID-19 impact on household energy consumption. Based on the hourly smart meter data, an average increase of 0.3% in the hourly average energy consumption is caused by a unit increase in the time-varying reproduction number of COVID-19. For each income, mid-income groups show less impact on energy consumption compared to both low-income and high-income groups. In each family member, as the number of family members increases, the change in electricity consumption affected by social distancing tends to decrease. For area groups, large area consumers increase household energy consumption more than other area groups. Lastly, The COVID-19 impact on each load shape is influenced by their energy consumption patterns.

Increased behind-the-meter (BTM) solar generation causes additional errors in short-term load forecasting. To ensure power grid reliability, it is necessary to consider the influence of the behind-the-meter distributed resources. This study proposes a method to estimate the size of behind-the-meter assets by region to enhance load forecasting accuracy. This paper proposes a semi-supervised approach to BTM capacity estimation, including PV and battery energy storage systems (BESSs), to improve net load forecast using a probabilistic approach. A co-optimization is proposed to simultaneously optimize the hidden BTM capacity estimation and the expected improvement to the net load forecast. Finally, this paper presents a net load forecasting method that incorporates the results of BTM capacity estimation. To describe the efficiency of the proposed method, a study was conducted using actual utility data. The numerical results show that the proposed method improves the load forecasting accuracy by revealing the gross load pattern and reducing the influence of the BTM patterns.

A silicon photomultiplier (SiPM) is considered a next generation photo-sensor for low-light applications. In this study, we propose and develop the general concept of a multipurpose submillimeter gamma-ray (MSG) imaging module that is applicable to most gamma-ray imaging devices. The main features of the MSG module are submillimeter intrinsic resolution, geometrically expandable structure, expandable data acquisition system, multi-energy capability and usability in intense magnetic fields. The elements of the gamma-ray detector are pixelated Ce:GAGG ( $31\times 31$ pixels, 0.8 mm pitch), a 1.5-mm acrylic light guide, and one SiPM array ( $8 \times 8$ pixels). The readout electronics are composed of a symmetric charge division resistive network, 16 channel preamplifiers, multiplexing amplifiers, and analog-to-digital converter (ADC) circuits. We use a subminiature field-programmable gate array (FPGA) board as a data acquisition system. To make the system more expandable, we apply a 10 GbE-based tree structure using a FPGA-based data acquisition board and switching hub. To verify multipurpose capabilities, the energy spectra and the flood images are obtained using Co-57, Na-22, and Cs-137 isotopes. We measure temperature characteristics of the detector module. A pinhole image is obtained to confirm high resolution property. The maximal data transfer rate that is measured using dummy gamma-ray event packets is 963.143 Mbps. This detector module can be applied to nuclear medicine imaging devices and radiation monitoring systems.

In a medium-voltage power system, fault currents are subjected to uncertainties in lines and distributed generations. Uncertainties in fault currents in a power grid can introduce system protection problems. Superconducting cable with fault current limiting capability can be used as means to meet the increasing electricity demand while resolving the issues involving system protection. In order to integrate the fault current limiting superconducting cable into a power grid, its impedance selection under fault conditions needs to consider the impact of power system uncertainties on system protection. In this paper, an expected regret-based method is proposed for determining the impedance of the fault current limiting superconducting cable incorporating the uncertainties in fault currents. The results of numerical simulations are presented to show the effectiveness of the proposed expected regret-based impedance selection method.

Urban DC railways have played an important role in urban transportation, and railway operation and energy management have been emphasized to be important to achieve maximum efficiency from existing infrastructure. Fast, accurate DC railway power flow analysis is required to operate a DC railway efficiently, and this paper proposes a novel methodology to analyze the DC railway power network by implementing the participation factor (PF). The PF concept consists of a correlation between the vehicle and the substation, as explained using zones based on the position of the vehicles running on the route. Then, the DC railway powerflow is calculated using the PF concept. Finally, the usability of the proposed method was validated via case studies with actual railway operation data, and we discuss some of the advantages of the suggested methodology.

Virtual power plants (VPPs) have been widely researched to handle the unpredictability and variable nature of renewable energy sources. The distributed energy resources are aggregated to form into a virtual power plant and operate as a single generator from the perspective of a system operator. Power system operators often utilize the incentives to operate virtual power plants in desired ways. To maximize the revenue of virtual power plant operators, including its incentives, an optimal portfolio needs to be identified, because each renewable energy source has a different generation pattern. This study proposes a stochastic mixed-integer programming based distributed energy resource allocation method. The proposed method attempts to maximize the revenue of VPP operators considering market incentives. Furthermore, the uncertainty in the generation pattern of renewable energy sources is considered by the stochastic approach. Numerical results show the effectiveness of the proposed method.

This paper presents a stochastic method for an electric vehicle (EV) aggregator to coordinate EV charging schedule considering uncertainty in EV departures. The EV aggregator is responsible for managing the charging schedule of EVs while participating in the electricity markets. The managed EV charging can provide additional revenues to the aggregator from regulation market participation and charging cost reductions to EV owners. The aggregator needs to coordinate the charging schedule considering various uncertain factors such as electricity market prices and the stochastic characteristics of EVs. In this paper, the EV charging scheduling problem incorporating uncertainty in EV departures is formulated as a stochastic optimization problem. A stochastic optimization method is used to solve the EV charging scheduling problem. Latin hypercube sampling (LHS) and a scenario-reduction method are also applied to reduce the computational efforts of the proposed method. The results of a numerical example are presented to show the effectiveness of the proposed stochastic EV charging coordination method.

Demand-side management (DSM) is easy to apply to reduce system peak load by a utility and it can be a convenient way to control and change amount of electric usage by end-use customers. Planning and operating techniques for a DSM program are required to efficiently manage and operate the program. This paper is focused on planning technique for an incentive-based DSM program. This paper describes a stochastic model that can estimate the operating days, hours, and total capacity for efficiently planning a DSM program. A temperature stochastic process, from weather derivatives, is used in the proposed method. Temperature sensitivity is proposed to improve load forecasting accuracy. The generalized extreme value distribution is also proposed for estimating stochastic results. The results of case studies are presented to show the effectiveness of the proposed method.

Superconducting cables can be used as an alternative to replace the conventional ac cables in a power system. Because of the characteristics of superconducting cables, their installation in a power system can influence the fault current levels and patterns. These changes in fault current levels and patterns can further have an impact on power system protection. Therefore, in order to prevent resulting problems, there is an increasing need to analyze the impact of a superconducting cable on the power system and determine its impedance under fault conditions. This paper presents a systematic impedance selection method for a superconducting cable under fault conditions, based on the evaluation results of the impact of superconducting cables on power system protection. Numerical simulations are carried out to demonstrate the effectiveness of the proposed method.

High-temperature superconducting (HTS) power cables offer significant advantages for long-distance high-capacity transmission. In order to meet the increasing demand in a power system, HTS power cables can be utilized for effective transmission expansion planning. However, when a fault occurs in a power system, the low impedance of the HTS cables can have an adverse impact on the system protection. In order to resolve the fault current issue, fault current limiting (FCL) HTS cables can be introduced for transmission expansion planning. The introduction of FCL-HTS cables can lead to an economical operation of a power system. This paper presents an economic evaluation method for the FCL-HTS cables in transmission expansion. A numerical example is presented to show the effectiveness of the proposed method.

The placement of the superconducting fault current limiter (SFCL) can be utilized to accommodate the increasing integration of distributed generations (DGs) in a power system. The installation of DGs in a power system can affect the power flow and the fault current patterns. Substantial changes in fault currents can cause coordination problems among the overcurrent relays. SFCL placement is a possible approach for accommodating the DGs by locally limiting the fault currents resulting from the DGs, while maintaining the existing relay settings. This paper presents a systematic procedure for analyzing the impacts of SFCL placement on DG expansion, considering the relay coordination in power systems.

Superconducting fault current limiters (SFCLs) are increasingly being used as a mechanism to effectively control fault currents in a power system. However, SFCL placement and relay coordination can be complicated by the unexpected failure or maintenance of SFCLs. This paper presents a power system reconfiguration method for coordinating overcurrent protection considering SFCL failure in system protection. The proposed power system reconfiguration method is designed to handle SFCL failure in the relay coordination.

In a power grid, the distributed generations (DGs) are not generally controlled by the system operator. Fault current levels in the power grid may change because of unpredictable changes in the status of noncontrollable DGs. These changes in the fault current can have an impact on the relay coordination. In this paper, the uncertainty associated with the connection status of DGs is reflected in the relay coordination. Superconducting fault current limiter (SFCL) placement can be effective for reducing the fault current while eliminating misoperation of the relays caused by DGs. However, an SFCL may also have a negative impact on the operation of the relays. An effective SFCL placement method is needed because of the high cost of the SFCL and the impact on the power system protection. In this paper, an SFCL placement method based on the minimax regret criterion is proposed for power system protection considering the uncertainty of the DGs.

Superconducting fault current limiters (SFCLs), which are used to restrict the maximum fault current level in a power system, can contribute to the economic operation of a power system by reducing congestion costs and expected energy not served costs. However, various power system factors such as load and fuel price need to be taken into account because these factors result in an economic risk when SFCLs are installed in a power system. Therefore, it is needed to carry out an economic assessment of SFCLs considering both the expected benefit and the risk. This paper presents a risk-based economic assessment method for an SFCL installation under power system uncertainties. In the paper, the conditional value-at-risk (CVaR) is used to estimate the economic risk due to power system uncertainties when installing SFCLs. The proposed risk assessment method for SFCL installation can be useful as a decision-making tool.

Various load disaggregation methods have been developed for monitoring home appliance loads to save energy in a smart home. Most of load disaggregation methods are designed to focus on the on/off events of single appliance. In reality, however, multiple appliances can be turned on/off simultaneously. Load disaggregation can be complicated by the simultaneous on/off events of multiple appliances. This paper presents a cepstrum-smoothing-based load disaggregation method to effectively deal with the simultaneous on/off events of multiple appliances. Further, a data acquisition system is developed to obtain only the characteristic signals of appliances and to filter the noise inputted from the power supply. Test results are provided to demonstrate the effectiveness of the proposed cepstrumsmoothing- based load disaggregation method1.

Since the demand response (DR) market was introduced in Korea, load aggregators have also been allowed to participate in the electricity market. However, a risk-management-based method for the efficient operation of demand response resources (DRRs) has not been studied from the load aggregators’ perspective. In this paper, a systematic DRR allocation method is proposed for load aggregators to operate DRRs using mean-variance portfolio theory. The proposed method is designed to determine the lowest-risk DRR portfolio for a given level of expected return using mean-variance portfolio theory from the perspective of load aggregators. The numerical results show that the proposed method can be used to reduce the risk compared to that obtained by the baseline method, in which all individual DRRs are allocated in a DRR group by maximum curtailment capability.

The spectroscopic radiation portal monitor (SPM) is widely used for homeland security. Many research groups are studying the radionuclide identification method which is one of the most important factors in the performance of the SPM using the large size of a thallium activated sodium iodide (NaI(Tl)) detector. In this study, we developed the radionuclide identification method for the SPM for pedestrian screening using a single NaI(Tl) detector that is small in size (2 in.), which is much smaller than those in the existing studies under the low signal-to-noise-ratio (SNR) condition. From the anomalous radionuclide spectrum, the noise component was effectively reduced by the wavelet decomposition and the proposed background subtraction method, and the signal component was enhanced by the principal component analysis. Finally, peak locations which have been determined by the peak search algorithm with a valley check method were compared with a pre-calibrated and constructed radionuclide database. To verify the radiation identification performance of the proposed method, experiments with various kinds of sources ( 137 Cs, 133 Ba, 22 Na, and 57 Co) and different SNR values (from distances of 10–150 cm and for scan times of 1–5 s) were performed. Although the high-SNR condition was explored as well, most experiments were conducted under the low-SNR condition to verify the robustness and reproducibility of the proposed algorithm. The results showed that over 98.3% of the single radionuclide detection rate was achieved regardless of which radionuclides were used, up to 50 cm under the worst SNR condition (1 s of scan time) and up to 90 cm under the best SNR condition (5 s of scan time). Furthermore we achieved accurate identification of multiple radionuclides at 40 cm with only 1 s of scan time. The results show that the proposed algorithm is competitive with the commercial method and our radionuclide identification method can be successfully applied to the SPM for pedestrian monitoring, with a small detector size and a short scan time.