Testing a model predictive control algorithm for a PV-CHP hybrid system on a laboratory test-bench

T.M. Kneiske, F. Niedermeyer and C. Boelling

Applied Energy, 2019, vol. 242, issue C, 137 pages

Abstract: In order to reduce the global warming to less than two degrees, a large increase in renewable energy resources like photovoltaic systems is necessary. A business case based on feed-in tariffs does not exist in every countries and self-consumption is limited due to the unsteady nature of solar radiation on earth. A combination with other systems such as batteries, heat-pumps or even combined heat and power plants can enhance the use of generated power by photovoltaic systems, particularly in private households and small businesses. Rule-based controllers and optimization algorithms (model predictive control) can both realise the efficient operation of a photovoltaic system in combination with storage systems and a combined heat and power plant. However, different controllers and energy management systems have hitherto only been compared theoretically. A comparison of such controllers in a real, controllable hardware environment has not yet been carried out. In this study, a test-bench is introduced to test different control algorithms for photovoltaic systems in combination with storage systems and a micro- combined heat and power plant. The operation has been tested for a one day period. Key performance parameters have been derived and compared for a rule-based control, an optimized control and simulation results including a forecast. The results show that the operational costs can be reduced by 7.3% for the chosen test-period using the optimized algorithm in the laboratory compared to the same system with a rule-based control. The results also indicate that even under perfect forecast conditions the hardware, metering and energy management cause latencies and inaccuracies leading to deviations, which are not accounted for in simulations. Hence, the accuracy of the forecast methods need not be higher than the deviations introduced by the hardware. These deviations often lead to unwanted charging and discharging events of the battery. A faster way of processing data and a second order or low level control is needed for short term reaction and higher efficiency.

Keywords: PV; CHP; Battery; Model predictive control; Energy management; Data analysis; Laboratory (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261919303988
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:242:y:2019:i:c:p:121-137

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.2019.03.006

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 ().