Transactive control of a residential community with solar photovoltaic and battery storage systems

4th Asia Conference of International Building Performance Simulation Association

土木建筑工程

Yu, Danilo^1 ; Brookson, Aidan^2 ; Fung, Alan S^3 ; Raahemifar, Kaamran^1 ; Mohammadi, Farahnaz^1

Electrical Engineering Department, Ryerson University, 350 Victoria St., Toronto

ON

M5B 2K3, Canada^1

Toronto and Region Conservation Authority (TRCA), 101 Exchange Ave., Vaughan

ON

L4K 5R6, Canada^2

Mechanical Engineering Department, Ryerson University, 350 Victoria St., Toronto

ON

M5B 2K3, Canada^3

关键词: Battery storage system;    Distributed Energy Resources;    Efficiency improvement;    Mechanical equipment;    Mixed-integer linear programming techniques;    Residential communities;    Solar photovoltaic energies;    Transactive controls;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/238/1/012051/pdf
DOI  :  10.1088/1755-1315/238/1/012051

学科分类：土木及结构工程学

来源: IOP

【 摘 要 】

Transactive energy is a two-way exchange of energy between the electric power grid and a community's distributed energy resources, offering opportunities for efficiency improvements through market-based economic and control techniques. A community's distributed energy resources include electricity-producing resources and controllable loads. Increased usage of unsynchronized generation of non-dispatchable solar photovoltaic energy and household demand at the community level can adversely affect the power quality, reliability and network balancing of the electricity grid. A solution was developed in this paper in the form of energy storage and demand side management on a solar residential community. An agent-based transactive energy management system was developed and simulated using multiple prosumer houses with roof-top PV systems and local energy storage. Experimental work conducted on an archetype house, near Toronto, Ontario, Canada, was used to model an all-electric residential house and clusters were created with varying orientations and building properties to mimic different efficiency levels of the houses within the virtual community. A machine learning algorithm using historical data and weather forecasts from Natural Resources Canada (NRCan) was used to forecast the community's energy generation as well as building's thermal loads. In this community, consumers can curtail their loads based on price signals sent to smart devices in homes. Open-loop mixed integer linear programming technique (MILP) and model predicted control (MPC) were compared and evaluated. The simulation shows promising results with a 9% energy savings during the summer solstice day and 5% during the winter solstice day when compared to the normal operation of houses' mechanical equipment.