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The rapid spread of renewable energy resources has increased need for demand flexibility as one of the solutions to power system imbalance. However, to properly estimate the demand flexibility, demand characteristics must be analyzed first and the corresponding flexibility measures must be validated. Thus, in this study, a novel approach is proposed to evaluate the demand flexibility provided by Electric Vehicle Chargers (EVC) in the residential sector based upon a new process of electric charging demand characteristic data analysis. The proposed model estimates the frequency, consistency, and operation scores of the flexible demand resource (FDR) during identified ramp-up/down intervals presented in our previous work. The scores are included in the components that calculate the flexibility score referring that the closer it is to 1, the higher utilization as an FDR. A case study was conducted by considering EV user segmentation based on their demand characteristic analysis. The results confirm that flexibility scores of segmented EVC groups are about 0.0273 in ramp-up and ramp-down intervals. Based on the experimental results, the flexibility score can be utilized for multi-dimensional analysis and verification in perspectives of seasonality, participation time interval, customer group classification, and evaluation. Thus, the proposed method can be used as an indicator to determine how a segmented EVC group is adequate to participate as an FDR while suggesting meaningful implications through EVC demand data analysis.
Keon Baek; Sehyun Kim; Eunjung Lee; Yongjun Cho; Jinho Kim. Data-Driven Evaluation for Demand Flexibility of Segmented Electric Vehicle Chargers in the Korean Residential Sector. Energies 2021, 14, 866 .
AMA StyleKeon Baek, Sehyun Kim, Eunjung Lee, Yongjun Cho, Jinho Kim. Data-Driven Evaluation for Demand Flexibility of Segmented Electric Vehicle Chargers in the Korean Residential Sector. Energies. 2021; 14 (4):866.
Chicago/Turabian StyleKeon Baek; Sehyun Kim; Eunjung Lee; Yongjun Cho; Jinho Kim. 2021. "Data-Driven Evaluation for Demand Flexibility of Segmented Electric Vehicle Chargers in the Korean Residential Sector." Energies 14, no. 4: 866.
Various incentive schemes are being implemented to improve the economic return of distributed energy resources (DERs). Accordingly, research on the optimal capacity design and operations of photovoltaic (PV) power generation and energy storage systems (ESSs) is important to ensure the economic efficiency of DERs. This study presents the models of optimal capacity and facility operation methods based on long-term operational changes of DERs in a building with self-consumption. Key policy variables are derived for a renewable energy system. We first analyzed the operating environments of the DERs according to the basic types of PVs and ESSs, and by examining the detailed benefit structure of a special rate for renewable energy. The optimal capacity of PVs and ESSs with the lowest net cost was estimated using various parameters in consideration of long-term operations (~15 years), and by setting rules for a special rate for renewable energy. It was confirmed that the combined use of peak and rate reductions constituted the most economical operational approach. A case study confirmed the economic sensitivity of cost and benefit analyses based on actual load data. Correspondingly, it is inferred that this study will identify core policy variables that can aid decision-making in the long-term perspective.
Jihun Jung; Keon Baek; Eunjung Lee; Woong Ko; Jinho Kim. Economic Analysis of Special Rate for Renewable Energy Based on the Design of an Optimized Model for Distributed Energy Resource Capacities in Buildings. Energies 2021, 14, 645 .
AMA StyleJihun Jung, Keon Baek, Eunjung Lee, Woong Ko, Jinho Kim. Economic Analysis of Special Rate for Renewable Energy Based on the Design of an Optimized Model for Distributed Energy Resource Capacities in Buildings. Energies. 2021; 14 (3):645.
Chicago/Turabian StyleJihun Jung; Keon Baek; Eunjung Lee; Woong Ko; Jinho Kim. 2021. "Economic Analysis of Special Rate for Renewable Energy Based on the Design of an Optimized Model for Distributed Energy Resource Capacities in Buildings." Energies 14, no. 3: 645.
The rapid increase in renewable energy resources has resulted in the increasing need for a demand flexibility program (DFP) from industrial load resources as a solution to oversupply and peak load spikes. However, to reasonably estimate the DR potential flexibility, the load characteristics must be analyzed and potential assessment formulas must be validated. Thus, in this study, a novel method is proposed to evaluate the DR potential flexibility of industrial loads according to a process of related load-characteristic data analysis. The proposed potential-estimation model considers frequency, consistency, and DR event operation scores during designated ramp-up and ramp-down time intervals separately. A case study was conducted by considering typical cement industry process with actual power-consumption data analysis for demonstrating the test system. The results confirm that load reduction of more than half of the usual power consumption is possible if a potential score is about 0.27 in cement industry cases. Thus, the proposed method can be used as an indicator to determine how an industrial load is adequate for obtaining a DFP while suggesting meaningful implications through industrial load-resource data analysis.
Eunjung Lee; Keon Baek; Jinho Kim. Evaluation of Demand Response Potential Flexibility in the Industry Based on a Data-Driven Approach. Energies 2020, 13, 6355 .
AMA StyleEunjung Lee, Keon Baek, Jinho Kim. Evaluation of Demand Response Potential Flexibility in the Industry Based on a Data-Driven Approach. Energies. 2020; 13 (23):6355.
Chicago/Turabian StyleEunjung Lee; Keon Baek; Jinho Kim. 2020. "Evaluation of Demand Response Potential Flexibility in the Industry Based on a Data-Driven Approach." Energies 13, no. 23: 6355.
In the smart grid environment, the penetration of electric vehicle (EV) is increasing, and dynamic pricing and vehicle-to-grid technologies are being introduced. Consequently, automatic charging and discharging scheduling responding to electricity prices that change over time is required to reduce the charging cost of EVs, while increasing the grid reliability by moving charging loads from on-peak to off-peak periods. Hence, this study proposes a deep reinforcement learning-based, real-time EV charging and discharging algorithm. The proposed method utilizes kernel density estimation, particularly the nonparametric density function estimation method, to model the usage pattern of a specific charger at a specific location. Subsequently, the estimated density function is used to sample variables related to charger usage pattern so that the variables can be cast in the training process of a reinforcement learning agent. This ensures that the agent optimally learns the characteristics of the target charger. We analyzed the effectiveness of the proposed algorithm from two perspectives, i.e., charging cost and load shifting effect. Simulation results show that the proposed method outperforms the benchmarks that simply model usage pattern through general assumptions in terms of charging cost and load shifting effect. This means that when a reinforcement learning-based charging/discharging algorithm is deployed in a specific location, it is better to use data-driven approach to reflect the characteristics of the location, so that the charging cost reduction and the effect of load flattening are obtained.
Jaehyun Lee; Eunjung Lee; Jinho Kim. Electric Vehicle Charging and Discharging Algorithm Based on Reinforcement Learning with Data-Driven Approach in Dynamic Pricing Scheme. Energies 2020, 13, 1950 .
AMA StyleJaehyun Lee, Eunjung Lee, Jinho Kim. Electric Vehicle Charging and Discharging Algorithm Based on Reinforcement Learning with Data-Driven Approach in Dynamic Pricing Scheme. Energies. 2020; 13 (8):1950.
Chicago/Turabian StyleJaehyun Lee; Eunjung Lee; Jinho Kim. 2020. "Electric Vehicle Charging and Discharging Algorithm Based on Reinforcement Learning with Data-Driven Approach in Dynamic Pricing Scheme." Energies 13, no. 8: 1950.
Due to the heterogeneity of demand response behaviors among customers, selecting a suitable segment is one of the key factors for the efficient and stable operation of the demand response (DR) program. Most utilities recognize the importance of targeted enrollment. Customer targeting in DR programs is normally implemented based on customer segmentation. Residential customers are characterized by low electricity consumption and large variability across times of consumption. These factors are considered to be the primary challenges in household load profile segmentation. Existing customer segmentation methods have limitations in reflecting daily consumption of electricity, peak demand timings, and load patterns. In this study, we propose a new clustering method to segment customers more effectively in residential demand response programs and thereby, identify suitable customer targets in DR. The approach can be described as a two-stage k-means procedure including consumption features and load patterns. We provide evidence of the outstanding performance of the proposed method compared to existing k-means, Self-Organizing Map (SOM) and Fuzzy C-Means (FCM) models. Segmentation results are also analyzed to identify appropriate groups participating in DR, and the DR effect of targeted groups was estimated in comparison with customers without load profile segmentation. We applied the proposed method to residential customers who participated in a peak-time rebate pilot DR program in Korea. The result proves that the proposed method shows outstanding performance: demand reduction increased by 33.44% compared with the opt-in case and the utility saving cost in DR operation was 437,256 KRW. Furthermore, our study shows that organizations applying DR programs, such as retail utilities or independent system operators, can more economically manage incentive-based DR programs by selecting targeted customers.
Eunjung Lee; Jinho Kim; Dongsik Jang. Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study. Energies 2020, 13, 1348 .
AMA StyleEunjung Lee, Jinho Kim, Dongsik Jang. Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study. Energies. 2020; 13 (6):1348.
Chicago/Turabian StyleEunjung Lee; Jinho Kim; Dongsik Jang. 2020. "Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study." Energies 13, no. 6: 1348.
In electricity markets, energy storage systems (ESSs) have been widely used to regulate frequency in power system operations. Frequency regulation (F/R) relates to the short-term reserve power used to balance the real-time mismatch of supply and demand. Every alternating current power system has its own unique standard frequency level, and frequency variation occurs whenever there is a mismatch of supply and demand. To cope with frequency variation, generating units—particularly base-loader generators—reduce their power outputs to a certain level, and the reduced generation outputs are used as a generation reserve whenever frequency variation occurs in the power systems. ESSs have recently been implemented as an innovative means of providing the F/R reserve previously provided by base-loader generators, because they are much faster in responding to frequency variation than conventional generators. We assess the economic benefits of ESSs for F/R, based on a new forecast of long-term electricity market price and real power system operation characteristics. For this purpose, we present case studies with respect to the South Korean electricity market as well as simulation results featuring key variables, along with their implications vis-à-vis electricity market operations.
Eunjung Lee; Jinho Kim; Lee; Kim. Assessing the Benefits of Battery Energy Storage Systems for Frequency Regulation, Based on Electricity Market Price Forecasting. Applied Sciences 2019, 9, 2147 .
AMA StyleEunjung Lee, Jinho Kim, Lee, Kim. Assessing the Benefits of Battery Energy Storage Systems for Frequency Regulation, Based on Electricity Market Price Forecasting. Applied Sciences. 2019; 9 (10):2147.
Chicago/Turabian StyleEunjung Lee; Jinho Kim; Lee; Kim. 2019. "Assessing the Benefits of Battery Energy Storage Systems for Frequency Regulation, Based on Electricity Market Price Forecasting." Applied Sciences 9, no. 10: 2147.
Recent demand response (DR) research efforts have focused on reducing the peak demand, and thereby electricity prices. Load reductions from DR programs can be viewed as equivalent electricity generation by conventional means. Thus, utility companies must pay incentives to customers who reduce their demand accordingly. However, many key variables intrinsic to residential customers are significantly more complicated compared to those of commercial and industrial customers. Thus, residential DR programs are economically difficult to operate, especially because excess incentive settlements can result in free riders, who get incentives without reducing their loads. Improving baseline estimation accuracy is insufficient to solve this problem. To alleviate the free rider problem, we proposed an improved two-step method—estimating the baseline load using regression and implementing a minimum-threshold payment rule. We applied the proposed method to data from residential customers participating in a peak-time rebate program in Korea. It initially suffered from numerous free riders caused by inaccurate baseline estimation. The proposed method mitigated the issue by reducing the number of free riders. The results indicate the possibility of lowering the existing incentive payment. The findings indicate that it is possible to run more stable residential DR programs by mitigating the uncertainty associated with customer electricity consumption.
Eunjung Lee; Dongsik Jang; Jinho Kim. A Two-Step Methodology for Free Rider Mitigation with an Improved Settlement Algorithm: Regression in CBL Estimation and New Incentive Payment Rule in Residential Demand Response. Energies 2018, 11, 3417 .
AMA StyleEunjung Lee, Dongsik Jang, Jinho Kim. A Two-Step Methodology for Free Rider Mitigation with an Improved Settlement Algorithm: Regression in CBL Estimation and New Incentive Payment Rule in Residential Demand Response. Energies. 2018; 11 (12):3417.
Chicago/Turabian StyleEunjung Lee; Dongsik Jang; Jinho Kim. 2018. "A Two-Step Methodology for Free Rider Mitigation with an Improved Settlement Algorithm: Regression in CBL Estimation and New Incentive Payment Rule in Residential Demand Response." Energies 11, no. 12: 3417.