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Analysing the Peak Shaving Effect and the Increase in SelfConsumption and Self Sufficiencyof Battery Storage When Coupledto a Single Family House
Dalarna University, School of Technology and Business Studies, Energy Technology.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The aims of the study were to investigate the increase in self-consumption and self-sufficiency and the peak shaving effect of batteries when added to a PV system dimensioned to supply the electric demand of a low energy house built in Sweden. The simulated batteries were Li-ion and the study had a 15-minutes temporal resolution. The study took only the technical aspect of batteries into account and disregarded the economic aspect. A data set of measurements from two near-zero-energy houses built by the Research Institute of Sweden (RISE) was used as inputs. The first house provided measured load profile for an automated load and measured PV production from its 3.6 kW

p system, the second house provided a load profile for a normal human interactive load. The measured PV production from the 3.6 kWp PV system was compared to the PV production from a simulated 3.6 kWp system taken from PVsyst and System Advisor Model (SAM) and by using different weather data profiles for the simulations. The global irradiance values from the used weather profile data were compared to demonstrate the difference in its values and its effect on the simulated PV production. This comparison between the measured PV production and the simulated one was done because most of the PV simulation software does not take measured PV production as input but they simulate their own PV production based on their built-in weather data; including the software used in this study SAM. The first house with the automated load had an annual energy consumption of Ca. 3600 kWh / Year. The second house with its human interactive load had an annual energy consumption of Ca. 6000 kWh / Year. The load profile was taken as a whole and then divided to different types; heat pump, ventilation and remaining load which represents house hold services. The effect of the input load profile types and its temporal resolution was clarified; this effect came in consistency to what was concluded from the literature review. Different simulations were done varying battery sizes, voltage level, coupling method, dispatch algorithm and other parameter. Three different dispatch algorithms were used for the simulations; two are designed for peak shaving and are built-in tools in SAM while the third algorithm is called Target Zero and designed for maximizing self-consumption and self-sufficiency, it was found in a reference so it was executed in MS Excel. Each of the algorithms used was found to affect both the peaks and the self-consumption and self-sufficiency of the system after adding the batteries compared to before, one as a major effect and the other as a byproduct effect. The peak shaving results varied by varying the batteries and the dispatch algorithm used, for the peak shaving algorithms from SAM, a general decrease in peaks value was reached. For the Target Zero algorithm which optimizes on self-consumption and self-sufficiency, a decrease in the number of the peaks was reached. Both decreases happened by increasing battery sizes. For the self-consumption and self-sufficiency effect, an increase happened with its highest value for 7.2 kWh batteries and by using the three different algorithms. The effect of the load type was also demonstrated by comparing the simulations results for the heat pumps from both houses since both heat pumps were found to have the highest effect on the results. The study was concluded by emphasizing the added values of batteries when coupled to a behind-the-meter PV system. The study could have been more precise and added more information to this field if it had a 1 minute temporal resolution simulations, but patching one minute temporal resolution load profile takes a long time. Working with one minute load profile requires one minute weather profile for the PV simulation which is normally only commercial. Also, having a weather station installed at the house to measure the solar irradiance to be used in the simulation instead of using different weather profiles would have added more accuracy to this paper.

Place, publisher, year, edition, pages
2018.
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:du-26991OAI: oai:DiVA.org:du-26991DiVA, id: diva2:1178579
Available from: 2018-01-30 Created: 2018-01-30 Last updated: 2018-03-15

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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