Development of a Battery Sizing Tool for Nearly Zero Energy Buildings

Ahmadiahangar, Roya; Husev, Oleksandr; Blinov, A. N.; Karami, Hossein; Rosin, Argo

doi:10.1109/iecon43393.2020.9254557

Cited by 6 publications

(7 citation statements)

References 23 publications

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“…In recent years, battery storage technologies have seen rapid growth for the applications, such as PV-based storage systems, portable devices, industry, and electrical vehicles [29]. The most commonly used batteries are Lithium-Ion (Liion) batteries and nickel manganese cobalt oxide (NMC) batteries [3]. Over the years, due to advanced technological developments and bulk generation, the cost of batteries has been significantly reduced [30].…”

Section: Battery Energy Storage System (Bess)mentioning

confidence: 99%

“…Therefore, the sampling time considered in this study is taken as 1 hour. The current price of a battery in Estonian is around 100 €/kWh whereas the price of the PV system is around 0.4 €/W [3], [39]. Table 9 shows the economic analysis of all three load cases with PV installation and without the BESS.…”

Section: B Economic Analysismentioning

confidence: 99%

“…The payback period in the same study was found to be 18 years. An optimal battery size tool was designed and the payback of the BESS was found to be around 40 years [3]. However, the author reported that if there is a 10% increase in the electricity sales cost to the grid, the payback time will reduce by up to 10 years.…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

“…Therefore, the EU framework 2010/31/EU and its amended version 2018/844/EU defined the energy targets for 2050. As per this directive, all the newly constructed buildings in the EU after 2020 must be nearly zero-energy buildings (nZEBs) [3]. These buildings will be powered through renewable energy sources (RES) while having a minimal dependency on the grid [4].…”

Section: Introductionmentioning

confidence: 99%

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Battery Size Optimization With Customer PV Installations and Domestic Load Profile

et al. 2022

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Photovoltaic (PV) is a highly feasible solution for modern renewable energy-powered residential buildings in terms of deployment and cost reduction of utility bills. The installation of solar PV systems along with optimal battery energy storage systems (BESS) size is the most popular energy cost minimization solution and will continue to increase rapidly in the coming years considering the European Union (EU) framework for nearly zero energy buildings (nZEBs). The current methods lack BESS size optimization and a comprehensive solution to charge/discharge BESS from PV and the grid. The main goal is to be self-sufficient and sustainable while having minimal dependence on the electrical grid. Therefore, this paper presents an efficient energy management model and optimal size of the BESS as two key factors to effectively minimize the total energy consumption cost of the nZEBs while having a minimum dependence on the grid. The energy management system is developed using linear programming and solved using simplex and interior-point methods. In addition, a heuristic algorithm is presented to determine the optimized charging and discharging schedule for nZEBs. A detailed techno-economic analysis of the proposed system is conducted for the whole year (covering all four seasons summer, winter, spring, and autumn) considering three common residential building cases and three different electricity pricing methods. We determined that seasonal electricity pricing is the most suitable and economical option to schedule charging and discharging of BESS from the grid in terms of minimum total hours of grid usage, the maximum number of charging hours of BESS from the solar PV system, maximum BESS discharging hours to sell energy, the minimum number of BESS charging hours from the grid, maximum number of discharging hours for energy usage within nZEBs, maximum revenue earned, and peak electrical load reduction for the grid.

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Section: Battery Energy Storage System (Bess)mentioning

confidence: 99%

Section: B Economic Analysismentioning

confidence: 99%

Section: Comparison With Previous Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Battery Size Optimization With Customer PV Installations and Domestic Load Profile

et al. 2022

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“…Eltanay et al [24] incorporated load prioritisation in addition to load shifting technology and divided loads into two groups. In [25,26], the authors employed the methodology to derive the size of a battery energy storage system (BESS) for self-consumption optimally in windless times, and operated the BESS to maximise its technical and economic benefits. Distributed energy resources for households should consist of as many energy sources as possible [2,27] to improve economic parameters.…”

Section: Overview Of Literaturementioning

confidence: 99%

Modelling of Consumption Shares for Small Wind Energy Prosumers

et al. 2021

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This article describes a simulation of energy distribution in an average household where electricity is produced with a small wind generator or purchased from the public electricity grid. Numerical experiments conducted within an average of five minutes were performed using annual production and consumption graphs. Virtual storage devices, a water tank and a battery were used to buffer energy inside the household. The energy required for non-shiftable consumption and hot water consumption were taken directly from the utility grid. Surplus energy remaining from wind generator production after providing for consumption and storage needs were redirected there. A cover factor was used as a measure of the efficiency of energy distribution. One of the aims of the article was to determine by simulations the change of the cover factor in a virtually designed situation where the expected energy output of the wind generator was known in advance over one to three hours. The results found that for the configuration of the proposed nanogrid option, the positive results were readily achieved when the expected wind generator production was known an hour ahead. Then, the cover factor increased from 0.593 to 0.645. The side result of using projected/expected production is an increase in asymmetrical energy exchanges bilaterally between nanogrid and utility grid in favour of grid sales. Another finding was that the cover factor depended on the wind generator’s production intensity but less on the intensity of consumption within the household.It is hoped/expected that future research will address the prediction of output using mathematical methods.

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