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.

With the increasing use of renewable energy resources in power systems, it is necessary to overcome the limitations of these resources in terms of the supply and demand balance in high-voltage power systems. A viable solution is employing zero net energy community microgrids, which can manage the demand locally and minimize the impact to the power grid. To this end, this paper proposes an optimal scheduling method for a zero net energy community microgrid with customer-owned energy storage systems (CES). It is assumed that the microgrid operator operates in the CES market based on a bilateral contract. The CES aggregators constitute the CES and participate in the market. The aggregated CES (ACES) is considered as a single energy storage system in the proposed scheduling method. Numerical examples are presented to demonstrate the effectiveness of the proposed method.

Periodic preventive maintenance of generators is required to maintain the reliable operation of a power system. However, generators under maintenance cannot supply electrical energy to the power system; therefore, it is important to determine an optimal generator maintenance schedule to facilitate efficient supply. The schedule should consider various constraints of the reliability-based demand response program, power system security, and restoration. Determining the optimal generator maintenance schedule is generally formulated as a non-linear optimization problem, which leads to difficulties in obtaining the optimal solution when the various power system constraints are considered. This study proposes a generator maintenance scheduling (GMS) method using transformation of mixed integer polynomial programming in a power system incorporating demand response. The GMS method is designed to deal with various system requirements and characteristics of demand response within a power system. A case study is conducted using data from the Korean power system to demonstrate the effectiveness of the proposed method for determining the optimal maintenance schedule. The results show that the proposed GMS method can be used to facilitate the efficient and reliable operation of a power system, by considering the applicable system constraints.

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.