Home appliances connected to Internet-of-Things (IoT) platforms have been extensively installed in smart homes. In this context, home energy management systems (HEMSs) have emerged as a viable solution for reducing energy costs. Although several studies have analyzed the implementation of HEMSs, a majority of these studies were based on the installation of numerous sensors. Owing to the complexity and costs associated with the installation of multiple sensors, implementation of HEMSs in smart homes is challenging. This paper presents an energy management scheme for an HEMS that minimizes the energy cost in a smart home with data obtained from IoT-based appliances typically used in a smart home. Case studies were conducted to demonstrate the effectiveness of the proposed method. Prototype software (version 11) based on the proposed method for a HEMS and a test house with IoT-based appliances were also implemented in the case studies.

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.