Abstract
We propose a multi-time scale energy management framework for a smart photovoltaic (PV) system that can calculate optimized schedules for battery operation, power purchases, and appliance usage. A smart PV system is a local energy community that includes several buildings and households equipped with PV panels and batteries. However, due to the unpredictability and fast variation of PV generation, maintaining energy balance and reducing electricity costs in the system is challenging. Our proposed framework employs a model predictive control approach with a physics-based PV forecasting model and an accurately parameterized battery model. We also introduce a multi-time scale structure composed of two-time scales: a longer coarse-grained time scale for daily horizon with 15-minutes resolution and a shorter fine-grained time scale for 15-minutes horizon with 1-second resolution. In contrast to the current single-time scale approaches, this alternative structure enables the management of a necessary mix of fast and slow system dynamics with reasonable computational times while maintaining high accuracy. Simulation results show that the proposed framework reduces electricity costs up 48.1% compared with baseline methods. The necessity of a multi-time scale and the impact on accurate system modeling in terms of PV forecasting and batteries are also demonstrated.
Highlights
The reduction of CO2 emissions and achieving a sustainable future motivate a broader integration of such renewable energy sources as solar and wind into energy systems worldwide
We proposed a multi-time scale energy management framework for a smart photovoltaic (PV) system
A model predictive control (MPC) approach is employed that uses PV generation forecasting as input to deal with highly volatile PV generation
Summary
The reduction of CO2 emissions and achieving a sustainable future motivate a broader integration of such renewable energy sources as solar and wind into energy systems worldwide. The prediction model was integrated [21] for renewable generation and integrated the demand load, and the MIPbased MPC provides an effective solution for battery scheduling. A hierarchical EMS was developed [28] for dealing with day-ahead schedules and intra-hour adjustment in an office building that contained PV systems and batteries in electric vehicles None of these above papers addressed appliance scheduling. Our proposed framework’s multi-time scale structure successfully treats the fast and slow dynamics of energy management in one integrated optimization loop by dividing the time scale into two-time scales: coarse-grained and fine-grained In this way, the modeling capability and computational time are improved. The multi-time scale energy management framework presents real-time optimizing methodology to reduce electricity costs while taking into account a mix of such fast and slow dynamics as PV, energy demand fluctuations, and battery transient responses.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
