This study proposes a deep-reinforcement-learning (DRL)-based vehicle-to-grid (V2G) operation strategy that focuses on the dynamic integration of charging station (CS) status to refine solar power generation (SPG) forecasts. To address the variability in solar energy and CS status, this study proposes a novel approach by formulating the V2G operation as a Markov decision process and leveraging DRL to adaptively manage SPG forecast errors. Utilizing real-world data from the Korea Southern Power Corporation, the effectiveness of this strategy in enhancing SPG forecasts is proven using the PyTorch framework. The results demonstrate a significant reduction in the mean squared error by 40% to 56% compared to scenarios without V2G. Our investigation into the effects of blocking probability thresholds and discount factors revealed insights into the optimal V2G system performance, suggesting a balance between immediate operational needs and long-term strategic objectives. The findings highlight the possibility of using DRL-based strategies to achieve more reliable and efficient renewable energy integration in power grids, marking a significant step forward in smart grid optimization.

This paper addresses the challenge of accurately forecasting solar power generation (SPG) across multiple sites using a single common model. The proposed deep learning-based model is designed to predict SPG for various locations by leveraging a comprehensive dataset from multiple sites in the Republic of Korea. By incorporating common meteorological elements such as temperature, humidity, and cloud cover into its framework, the model uniquely identifies site-specific features to enhance the forecasting accuracy. The key innovation of this model is the integration of a classifier module within the common model framework, enabling it to adapt and predict SPG for both known and unknown sites based on site similarities. This approach allows for the extraction and utilization of site-specific characteristics from shared meteorological data, significantly improving the model’s adaptability and generalization across diverse environmental conditions. The evaluation results demonstrate that the model maintains high performance levels across different SPG sites with minimal performance degradation compared to site-specific models. Notably, the model shows robust forecasting capabilities, even in the absence of target SPG data, highlighting its potential to enhance operational efficiency and support the integration of renewable energy into the power grid, thereby contributing to the global transition towards sustainable energy sources.

With the widespread use of electric vehicles (EVs), the potential to utilize them as flexible resources has increased. However, the existing vehicle-to-grid (V2G) studies have focused on V2G operation methods. The operational performance is limited by the amount of availability resources, which represents the flexibility. This study proposes a data-driven modeling method to estimate the V2G flexibility. A charging station is a control point connected to a power grid for V2G operation. Therefore, the charging stations’ statuses were analyzed by applying the basic queuing model with a dataset of 1008 chargers (785 AC chargers and 223 DC chargers) from 500 charging stations recorded in Korea. The basic queuing model obtained the long-term average status values of the stations over the entire time period. To estimate the V2G flexibility over time, a charging station status modeling method was proposed within a time interval. In the proposed method, the arrival rate and service time were modified according to the time interval, and the station status was expressed in a propagated form that considered the current and previous time slots. The simulation results showed that the proposed method effectively estimated the actual value within a 10% mean absolute percentage error. Moreover, the determination of V2G flexibility based on the charging station status is discussed herein. According to the results, the charging station status in the next time slot, as well as that in the current time slot, is affected by the V2G. Therefore, to estimate the V2G flexibility, the propagation effect must be considered.

This study proposes a deep-reinforcement-learning (DRL)-based vehicle-to-grid (V2G) operation strategy that focuses on the dynamic integration of charging station (CS) status to refine solar power generation (SPG) forecasts. To address the variability in solar energy and CS status, this study proposes a novel approach by formulating the V2G operation as a Markov decision process and leveraging DRL to adaptively manage SPG forecast errors. Utilizing real-world data from the Korea Southern Power Corporation, the effectiveness of this strategy in enhancing SPG forecasts is proven using the PyTorch framework. The results demonstrate a significant reduction in the mean squared error by 40% to 56% compared to scenarios without V2G. Our investigation into the effects of blocking probability thresholds and discount factors revealed insights into the optimal V2G system performance, suggesting a balance between immediate operational needs and long-term strategic objectives. The findings highlight the possibility of using DRL-based strategies to achieve more reliable and efficient renewable energy integration in power grids, marking a significant step forward in smart grid optimization.

This paper addresses the challenge of accurately forecasting solar power generation (SPG) across multiple sites using a single common model. The proposed deep learning-based model is designed to predict SPG for various locations by leveraging a comprehensive dataset from multiple sites in the Republic of Korea. By incorporating common meteorological elements such as temperature, humidity, and cloud cover into its framework, the model uniquely identifies site-specific features to enhance the forecasting accuracy. The key innovation of this model is the integration of a classifier module within the common model framework, enabling it to adapt and predict SPG for both known and unknown sites based on site similarities. This approach allows for the extraction and utilization of site-specific characteristics from shared meteorological data, significantly improving the model’s adaptability and generalization across diverse environmental conditions. The evaluation results demonstrate that the model maintains high performance levels across different SPG sites with minimal performance degradation compared to site-specific models. Notably, the model shows robust forecasting capabilities, even in the absence of target SPG data, highlighting its potential to enhance operational efficiency and support the integration of renewable energy into the power grid, thereby contributing to the global transition towards sustainable energy sources.

With the widespread use of electric vehicles (EVs), the potential to utilize them as flexible resources has increased. However, the existing vehicle-to-grid (V2G) studies have focused on V2G operation methods. The operational performance is limited by the amount of availability resources, which represents the flexibility. This study proposes a data-driven modeling method to estimate the V2G flexibility. A charging station is a control point connected to a power grid for V2G operation. Therefore, the charging stations’ statuses were analyzed by applying the basic queuing model with a dataset of 1008 chargers (785 AC chargers and 223 DC chargers) from 500 charging stations recorded in Korea. The basic queuing model obtained the long-term average status values of the stations over the entire time period. To estimate the V2G flexibility over time, a charging station status modeling method was proposed within a time interval. In the proposed method, the arrival rate and service time were modified according to the time interval, and the station status was expressed in a propagated form that considered the current and previous time slots. The simulation results showed that the proposed method effectively estimated the actual value within a 10% mean absolute percentage error. Moreover, the determination of V2G flexibility based on the charging station status is discussed herein. According to the results, the charging station status in the next time slot, as well as that in the current time slot, is affected by the V2G. Therefore, to estimate the V2G flexibility, the propagation effect must be considered.

A virtual energy storage system (VESS) logically shares a physical energy storage system among multiple units. In resource sharing, the distribution of benefits is a critical problem. As a resolution, this study proposes a fair VESS operation method for smart energy communities that involve groups of energy consumption units. First, the cost and resource fairness indices are defined as the benefit and VESS usage proportional to the investment cost, respectively. The fair VESS operation problem is formulated considering the fairness indices that could be solved optimally by applying gradient methods without additional computational burden. The simulation results using the dataset in Korea demonstrate that the proposed operation allows the fair distribution of the benefit and resource usage among units with a marginal benefit reduction of approximately 5% in relation to the VESS operation to maximize the benefit. Moreover, it is shown that the resource fairness that controls the VESS usage limits the total benefit, and the cost fairness distributes the benefit among units according to the cost contribution. Furthermore, the proposed VESS operation can manage the VESS lifetime and improve the system performance of the utility grid.

This study presents a virtual energy storage system (VESS) scheduling method that strategically integrates fixed and dynamic energy storage (ES) solutions to optimize energy management in commercial buildings. Fixed ES, such as batteries, provides stable flexibility but is expensive and can be inefficiently operated. In contrast, dynamic ES can be utilized as needed but requires validation of their flexibility. By combining fixed ES with dynamic ES utilizing vehicle-to-grid (V2G) capabilities, this approach enhances grid stability and manages energy costs more effectively. Empirical validation using real-world data from Korea demonstrates significant improvements in total net benefits by reducing energy costs, which are crucial for the economic sustainability of commercial energy use. Additionally, the analysis of Pearson’s linear correlation coefficient with demand identifies where benefits occur in the scheduling process. The integrated system reduces the need for costly upgrades to the utility grid, suggesting a strategic advantage for large-scale adoption. This study establishes a framework for the broader implementation of such integrated systems, highlighting the potential for substantial improvements in energy efficiency, reduced carbon emissions, and enhanced grid reliability.

In this study, a community solar implementation strategy is proposed for smart energy community that consists of energy community members (ECMs) of an energy consumption group. The community solar implementation problem is expressed as a composition problem of a community member set. The problem is well known a NP-hard in which computational complexity increases exponentially as the size of the set increases. In this study, the necessary conditions for forming an energy community set are derived through the load relationship between individual ECMs and the relationship with solar power generation. Using that, an efficient community solar implementation strategy is proposed in which the computational complexity is linearly increased with the size of the ECM set. The simulation results show that the use of the proposed strategy achieves stable performance with a marginal benefit reduction of less than 9% compared to the optimal method. In addition, it is discussed that fairness in the distribution of net benefits among ECMs can be indirectly guaranteed through the use of price factors.

In order to utilize renewable power generation that contains uncertainty due to natural intermittence, it is essential to develop renewable power generation forecasting technologies. This paper focuses on identifying the technology development of artificial intelligence (AI)-based renewable power generation by the analysis of patents. In this study, AI-based renewable power generation forecasting technology is comprehensively defined, and related patent applications are systematically identified to analyze country, period, and technological trends. The results indicate that 1) renewable power generation forecasting has reached a technological maturity in Japan, the United States, and Europe, 2) to overcome this, AI technology has been being grafted onto renewable power generation forecasting, 3) the use of meteorological data was described as a major right, and AI technology was described as an applicable additional item due to the characteristics of AI technology patents.

Digital technologies, especially information and communication technologies, paved the way for social welfare by providing efficient and effective means for services. In the energy sector, advanced metering infrastructures (AMIs) are essential for providing various services through information measurement. In this article, we focus on the deployment of an AMI in multi-dwelling units where automated meter reading (AMR) infrastructures are installed. In particular, we explore whether the AMR should substitute the AMI with few alterations, while ensuring desirable accuracy. To determine the adequacy of technology, information measurement performance, service performance, and implementation cost are used as the indicators. Through a case study using real data recorded in Korea, we quantitatively estimate that AMR-based information measurement can exhibit adequate performance and performance degradation of less than 1% in a service environment utilizing AMI with a low-cost investment. We also discuss several technologies and implementation issues in the upcycling of AMR for more reliable service. This study provides a guide for when configuring an information measurement system for a new energy service.