Yousefi, Mojtaba



PROJECT TITLE: Stochastic Energy Management in Smart Home with Integration of PV/Plug-in Electric Vehicle and Battery Storage  


PhD period: 2017.04.01 – 2020.03.31.
Section: Esbjerg Energy Section 
Research Programmes: Photovoltaic Systems and Microgrids
Supervisor: Amin Hajizadeh
Co-Supervisor: Mohsen Soltani
Contact Information

Collaborator: AAU.
Funding: Self-financing.

ABSTRACT

Buildings have a profound influence on the natural environment, health, economy, and productivity. In Europe, buildings are responsible for two-fifths of energy consumption and 36% of the European Union’s emission. Hence, energy efficiency in buildings is essential to reduce global energy usage and improve the local environmental sustainability. The objective of smart home energy management (HEM) is to use energy efficiently for a comfortable and enjoyable living and working environment. Underlying this objective is the fundamental trade-off between costs and quality of services. The advance of “smart grid” will likely advance the state of the art of HEM in multiple dimensions. Some of the most important characteristics of smart HEM in a smart grid era include the extensive use of sensing devices, the optimal and automated management of different types of load, the integration of renewable energy and storage, and the ability to respond to dynamic prices. Therefore, this growing trend provides the technical foundation and infrastructure the smart home with home energy management system HEMS.

HEMS is an essential home system for successful demand-side management of smart grid. With the rapid development in advanced power electronics and alternative energy technologies, building renewable and stored energy sources installed at residential premises can be incorporated in HEMS to improve the in-home efficiency of energy conversion and utilization [1]. Hence, HEMS is defined as the optimal system providing energy management services in order to efficiently monitor and manage electricity generation, storage, and consumption in smart houses [2, 3].

Renewable energy has been consumed in various fields, including the industrial, residential, commercial, and public sectors. With the rapid development of sustainable energy technologies and increasing demand for low emission generations, the utilization of renewable energy shows promising prospects for smart houses. Thanks to its easy installation and low cost, the solar PV is widely used in smart houses. Panel power can vary rapidly due to weather condition, local intermittent shading, passing clouds or flocks of birds, differential soiling, and time of day. If uncontrolled, a significant impact on power grid may happen, including performance degradation, overloads, and over generation, especially when a larger scales distributing generation units and PEVs are used. Thus, stochastic dynamic energy management or stochastic optimization of such energy component is essential and recently researchers have focused on developing effective management for integrating PEVs and renewable energy into house loads and grid. Hence, this proposal focuses on novel strategies of home energy management system, such as using stochastic dynamic optimization (SDP) or stochastic model predictive control (MPC) for smart home with solar power supply and plug-in electrical vehicle (PEV) battery energy storage so that the daily electricity consumption cost of the smart home is minimized. For this purpose, new smart buildings will become integrated systems where renewable energy generation, especially Photovoltaic (PV) solar panels, electric vehicle charging, and onsite energy storage function together and interact effectively with the electricity grid.

PAPERS

Publications in journals and conference papers may be found at VBN.