Optimal operation of smart houses by a real-time rolling horizon algorithm

In this paper, a novel real-time rolling horizon optimization framework for the optimal operation of a smart household is presented. A home energy management system (HEMS) model based on mixed-integer linear programming (MILP) is developed in order to minimize the energy procurement cost considering that the household is enrolled in a dynamic pricing tariff scheme. Several assets such as a photovoltaic (PV) installation, an electric vehicle (EV) and controllable appliances are considered. Additionally, the energy from the PV and the EV can be used either to satisfy the household demand or can be sold back to the grid. The uncertainty of the PV production is estimated using time-series models and performing forecasts on a rolling basis. Also, appropriate distribution is used in order to model the uncertainty related to the EV. Besides, several parameters can be updated in real-time in order to reflect changes in demand and consider the end-user's preferences. The optimization algorithm is executed on a regular basis in order to improve the results against uncertainty

Coordinated operation of a neighborhood of smart households comprising electric vehicles, energy storage and distributed generation

In this paper, the optimal operation of a neighborhood of smart households in terms of minimizing the total energy procurement cost is analyzed. Each household may comprise several assets such as electric vehicles, controllable appliances, energy storage and distributed generation. Bi-directional power flow is considered both at household and neighborhood level. Apart from the distributed generation unit, technological options such as vehicle-to-home and vehicle-to-grid are available to provide energy to cover self-consumption needs and to inject excessive energy back to the grid, respectively. The energy transactions are priced based on the net-metering principles considering a dynamic pricing tariff scheme. Furthermore, in order to prevent power peaks that could be harmful for the transformer, a limit is imposed to the total power that may be drawn by the households. Finally, in order to resolve potential competitive behavior, especially during relatively low price periods, a simple strategy in order to promote the fair usage of distribution transformer capacity is proposed

Consideration of the impacts of a smart neighborhood load on transformer aging

Smart grid solutions with enabling technologies such as energy management systems (EMSs) and smart meters promote the vision of smart households, which also allows for active demand side in the residential sector. These technologies enable the control of residential consumption, local small-scale generation, and energy storage systems to respond to time-varying prices. However, shifting loads simultaneously to lower price periods is likely to put extra stress on distribution system assets such as distribution transformers. Especially, additional new types of loads/appliances such as electric vehicles (EVs) can introduce even more burden on the operation of these assets, which is an issue that needs special attention. Such extra stress can cause accelerated aging of distribution system assets and significantly affect the reliability of the system. In this paper, the impact of a smart neighborhood load on distribution transformer aging is investigated. The EMS of each household is designed to respond to prices and other signals emitted by the responsive load serving entity within the relevant demand response strategy. An optimization framework based on mixed-integer linear programming is presented in order to define the EMS structure. Then, the equivalent aging of the distribution transformer is examined with a thermal model under different scenarios. The case studies that are presented indicate that the integration of EVs in residential premises may indeed cause accelerated aging of the distribution transformers, while the need to investigate the efficiency of dynamic pricing mechanisms is rendered evident

Smart households and home energy management systems with innovative sizing of distributed generation and storage for customers

As a recently increasing trend among different applications of smart grid vision, smart households as a new implementation area of demand response (DR) strategies have drawn more attention both in research and in engineering practice. On the other hand, optimum sizing of renewable energy based small scale hybrid systems is also a topic that is widely covered by the existing literature. In this study, the sizing of additional distributed generation and energy storage systems to be applied in smart households, which due to DR activities have a different daily demand profile compared with normal household profiles, is investigated. To the best knowledge of the authors, this is the first attempt in the literature to consider the impact of DR on sizing. The study is conducted using a mixed-integer linear programming framework for home energy management system modeling and techno-economical sizing. Also, different sensitivity analyses considering the impacts of variation of economic inputs on the provided model are realized