Unit commitment of photovoltaic-battery systems: An advanced approach considering uncertainties from load, electric vehicles, and photovoltaic. (15th December 2020)

• Record Type: Journal Article
• Authors: Langenmayr, Uwe; Wang, Weimin; Jochem, Patrick
• Is Part Of: Applied energy. Volume 280(2020)
• Publication Date: 2020-12-15
• Subjects: PV-battery systems -- Peak shaving -- Uncertainty -- Monte Carlo simulation -- Electric vehicle -- Optimization; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.115972
• Languages: English
• ISSNs: 0306-2619

Abstract

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. Highlights: Peak demand is shaved for photovoltaic battery systems with electric vehicles. A central planner's objective is applied on a decentral operator's system. Uncertainties from load demand, photovoltaic, and electric vehicle are considered. A battery reserve capacity and the relaxation of the demand limit are implemented. A reduction of the daily peak by 17% to 52% is achieved.