Unit commitment of photovoltaic-battery systems: An advanced approach considering uncertainties from load, electric vehicles, and photovoltaic

Uwe Langenmayr, Weimin Wang and Patrick Jochem

Applied Energy, 2020, vol. 280, issue C, No S0306261920314227

Abstract: Increasing use of renewable energy leads to change in load flows from predictable generation and inelastic demand to more volatile and price-elastic patterns, especially on the distribution level. New applications such as electric vehicles further increase the demand of electricity. Therefore, a reliable, local control of load flexibilities is a key competence of future system operators. This paper presents a central planner–decentral operator approach to schedule local electricity flows. The central planner conducts a two-stage optimization to derive the demand limit and a corresponding battery schedule, while the decentral operator simply applies the battery schedule and heuristically reacts to unforeseen deviations between the forecasted and actual loads and power generation. Privacy concerns of the decentral planner are avoided as no private information is shared with the central planner. A relaxation factor and a reserve capacity for the battery are derived from a Monte Carlo simulation to consider the underlying uncertainties of load, photovoltaic generation, and electric vehicle charging. Our results show that the load of the decentral operator can be limited reliably for six days of the considered week and a maximum reduction of 2.6 kW (52%) of peakload has been accomplished. Furthermore, the approach is suitable for systems with limited computational resources at the place of the decentral operator, which is the common case in this field.

Keywords: PV-battery systems; Peak shaving; Uncertainty; Monte Carlo simulation; Electric vehicle; Optimization (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261920314227
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:280:y:2020:i:c:s0306261920314227

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2020.115972

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().