Dimensioning battery energy storage systems for peak shaving based on a real-time control algorithm. (15th December 2020)

• Record Type: Journal Article
• Authors: Lange, Christopher; Rueß, Alexandra; Nuß, Andreas; Öchsner, Richard; März, Martin
• Is Part Of: Applied energy. Volume 280(2020)
• Publication Date: 2020-12-15
• Subjects: Peak shaving -- Real-time control algorithm -- Electrical energy storage -- Battery dimensioning -- Energy system simulation; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.115993
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: In order to reduce power peaks in the electrical grid, battery systems are used for peak shaving applications. Under economical constraints, appropriate dimensioning of the batteries is essential. A dimensioning process is introduced consisting of a simulation environment to determine the behavior of the energy system, a real-time peak shaving control algorithm and an optimization process for detection of battery and algorithm parameters. The dimensioning process is investigated on the basis of four exemplary load profiles and in comparison to a conventional approach. Deviations between -7% and 75% for capacity and up to 43% for discharging power indicate undersized batteries using the conventional approach. The proposed approach relies on 1-min measurement data and does not require prediction data, leading to accurate dimensioning results for a given load profile, as verified in simulation. The practical use and effectiveness of the control algorithm is proven in a real-world laboratory. A battery system of 60 kWh capacity and 65 kW maximum power achieved successful peak load reduction by 50 kW (8%) for an a priori defined limit of 570 kW. The comparison with simulation shows only small deviations below 17 kW (4.1%) for the resulting load profile proving the realistic representation of an energy system in simulation. Highlights: Reducing electrical power peaks yields high energy cost savings. Conventional dimensioning approach results in up to 80% undersized capacities. Proposed approach provides accurate battery dimensioning for effective peak shaving. Consideration of real-time algorithm and battery constraints improves results. Test in real-world laboratory shows deviations below 4.1% compared to simulation.