Due to the recent development of information and communication technology (ICT), various studies using real-time data are now being conducted. The microgrid research field is also evolving to enable intelligent operation of energy management through digitalization. Problems occur when operating the actual microgrid, causing issues such as difficulty in decision making and system abnormalities. Using digital twin technology, which is one of the technologies representing the fourth industrial revolution, it is possible to overcome these problems by changing the microgrid configuration and operating algorithms of virtual space in various ways and testing them in real time. In this study, we proposed an energy storage system (ESS) operation scheduling model to be applied to virtual space when constructing a microgrid using digital twin technology. An ESS optimal charging/discharging scheduling was established to minimize electricity bills and was implemented using supervised learning techniques such as the decision tree, NARX, and MARS models instead of existing optimization techniques. NARX and decision trees are machine learning techniques. MARS is a nonparametric regression model, and its application has been increasing. Its performance was analyzed by deriving performance evaluation indicators for each model. Using the proposed model, it was found in a case study that the amount of electricity bill savings when operating the ESS is greater than that incurred in the actual ESS operation. The suitability of the model was evaluated by a comparative analysis with the optimization-based ESS charging/discharging scheduling pattern.

Confronted with the climate crisis, the world is making tremendous efforts in energy transition, such as expanding renewable energy that does not emit carbon. The importance of virtual power plant (VPP) operation technology has emerged to secure grid flexibility in response to the expanding renewable energy implemented due to these efforts. Accordingly, VPPs, which include photovoltaics, wind turbines, heating, ventilation, and air conditioning (HVAC), load, and EV, have been constructed. HVAC, one of the component resources, is a system that controls and regulates temperature, humidity, and airflow. Since it responds sensitively to the building’s heat capacity and changes in the external environment, it requires continuous and stable control. In this paper, we used data-based modeling to implement the HVAC required for the optimal operation of VPP. Since accurately creating an equation-based HVAC model was difficult considering building information modeling and external environment variables, we used historical HVAC operation data to perform data-based modeling. The model was implemented using nonlinear regression and machine learning, such as a support vector machine and artificial neural network. Then, the data-based HVAC and the actual HVAC operation results were comparatively analyzed based on a case study, and the model’s goodness-of-fit was evaluated based on performance metrics. Model performance indicators confirmed that the ANN-based HVAC model was most similar to the actual HVAC system.

Sector coupling technology, which is also called power-to-X (P2X) technology, refers to the conversion of renewable energy system (RES) outputs into various forms of energy, enhancing the utility of RESs and facilitating the development of sustainable energy systems. However, given the diverse characteristics of different P2X systems, the effective integration and operation of P2X resources are critical. This study aimed to propose a method for optimizing bidding capacities in power generation projects by integrating various P2X resources—including power-to-mobility, power-to-gas, and power-to-heat—as well as energy storage system (ESS) resources to improve flexibility and stabilize output. This study modeled the diverse characteristics of P2X resources and established objective functions and constraints. The optimization method for the integrated operational plan was developed using mixed integer linear programming. The results demonstrate that by considering the specific characteristics of each P2X and ESS resource, optimal resource allocation could effectively mitigate the variability of RES output and determine feasible bidding capacities. The proposed method is expected to contribute to mitigating RES variability, advancing sustainable energy transitions, reducing greenhouse gas emissions, and enhancing the flexibility of power systems.