Vertical free-swinging photovoltaic racking energy modeling: A novel approach to agrivoltaics

Hayibo, Koami Soulemane; Pearce, Joshua M.

doi:10.1016/j.renene.2023.119343

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…Hayibo and Pearce assumed a discount rate of 4.1% [58], whereas Poonia et al assumed a discount rate of 12.0% [55]. For the degradation rate, the estimation conditions varied significantly, ranging from 0.25%, a value assumed by Feuerbacher et al [62], to 3.10%, a value assumed by Trommsdorff et al [49].…”

Section: Discussionmentioning

confidence: 99%

“…Junedi et al found that the LCOE of agrivoltaics was significantly affected by factors such as life cycle, module efficiency, structural support, occupied space, installation type, PV location, and solar radiation, and they also estimated the LCOE based on the location of the agrivoltaics, rooftops, and buildings [57]. Hayibo and Pearce estimated the LCOE by considering panel tilt and seasonal factors [58], and Willockx et al suggested using the LCOE and optimal ground coverage ratio for the entire EU using geospatial data [59].…”

Section: Literature Reviewmentioning

confidence: 99%

“…This is an LCOE estimation study on an agrivoltaics demonstration project in South Korea, which also needs to be conducted in other countries for the purposes of comparative analysis. [37] 2013 Italy LCOE, logistic function Branker et al [31] 2011 Canada LCOE, sensitivity analysis Chudinzow et al [36] 2020 Germany LCOE, sensitivity analysis Dhass et al [60] 2020 India LCOE (PV materials) Gholami and Røstvik [40] 2021 Europe LCOE, NPV Hernández-Moro et al [45] 2013 Global LCOE, NPV, sensitivity analysis Lai and McCulloch [35] 2017 Kenya LCOE, LCOD Lee and Ahn [34] 2020 Korea LCOE, Monte Carlo simulation, sensitivity analysis Limmanee et al [32] 2017 Thailand LCOE Mundada et al [27] 2016 USA LCOE, sensitivity analysis NREL [28] 2016 USA LCOE Ondraczek [42] 2014 Kenya LCOE, sensitivity analysis Ondraczek et al [43] 2015 Global LCOE Ouyang and Lin [33] 2014 China LCOE Parrado et al [61] 2016 Chile LCOE Reichelstein and Yorston [29] 2013 USA LCOE, sensitivity analysis Reichenberg et al [41] 2018 EU LCOE, sensitivity analysis Rodríguez-Ossorio et al [38] 2021 Spain LCOE, sensitivity analysis Väisänen et al [39] 2019 Finland LCOE Vartiainen et al [44] 2020 EU LCOE, sensitivity analysis Agrivoltaics LCOE Agostini et al [50] 2021 Italy LCOE, NPV, IRR Ahmed et al [56] 2022 Global LCOE, NPV Bhandari et al [54] 2021 Niger LCOE, B/C ratio, NPV Cuppari et al [52] 2021 USA VAR, Ornstein-Uhlenbeck, NPV Dinesh and Pearce [53] 2016 USA LCOE Feuerbacher et al [46] 2021 Germany LCOE Feuerbacher et al [62] 2022 Germany LCOE Francia and Cupo [51] 2023 Italy LCOE, NPV, cost-benefit analysis Hayibo and Pearce [58] 2023 Canada LCOE, SAM model Junedi et al [57] 2022 Global LCOE, EPBT Poonia et al [55] 2022 India LCOE, IRR, BCR Schindele et al [47] 2020...…”

Section: Literature Reviewmentioning

confidence: 99%

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Estimating the Deterministic and Stochastic Levelized Cost of the Energy of Fence-Type Agrivoltaics

Hwang,

Lee

2024

Energies

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Agrivoltaics can be used to supply energy and produce agricultural products in order to meet the growing demand for energy and food. The amount of power generation is affected by the solar panel direction, spacing, tilt, and panel technology; however, there is insufficient empirical data-based research on the operation of agrivoltaics. This study estimates the levelized cost of energy (LCOE) for a fence-based agrivoltaics system using bifacial modules. This study installed and operated photovoltaic (PV) systems on a rice paddy and saltern in South Korea to estimate the input variables that could affect their economic efficiency and LCOE. For the research methods, this study used Monte Carlo simulation (a stochastic analysis method that reflects the uncertainty of the input variables), a deterministic LCOE analysis, and a sensitivity analysis of the input variables. In terms of space utilization, the LCOE of the paddy system (139.07~141.19 KRW/kWh) was found to be relatively lower than that of the saltern system (145.43~146.18 KRW/kWh), implying that the PV system on the paddy was economically favorable. In terms of installation direction, it was more economical to operate the southwest-facing panels (139.07~145.43 KRW/kWh) than the southeast-facing panels (141.19~146.18 KRW/kWh). This study provides foundational policy data for the adoption of fence-based agrivoltaics and contributes to the widespread and active use of agrivoltaics.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Estimating the Deterministic and Stochastic Levelized Cost of the Energy of Fence-Type Agrivoltaics

Hwang,

Lee

2024

Energies

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“…The solar PV systems sizing is performed using a new design approach proposed in a recent study [40] to calculate the optimal size of PV for supplying the residential thermal load. This new approach relies on the strength of SAM [67] developed by NREL [67] and uses the flexibility of a Python implementation of SAM [104,105] to design and evaluate the performance of a PV system coupled with a load.…”

Section: Solar Pv Sizingmentioning

confidence: 99%

Residential Sizing of Solar Photovoltaic Systems and Heat Pumps for Net Zero Sustainable Thermal Building Energy

Rana,

Jamil,

Asgari

et al. 2024

Preprint

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To enable net zero sustainable thermal building energy this study develops an open-source thermal house model to couple solar photovoltaic (PV) and heat pumps (HP) for grid-connected residential housing. The calculation of both space heating and cooling thermal loads and the selection of HP is accomplished with a validated Python model for air-source heat pumps. The capacity of PV required to supply the HPs is calculated using an System Advisor Model integrated Python model. Self-sufficiency and self-consumption of PV and the energy imported/exported to the grid for a case study are provided, which shows that simulations based on the monthly load profile have a significant reduction of 43% for energy sent to/from the grid compared to the detailed hourly simulation and an increase from 30% to 60% for self-consumption and self-sufficiency. These results show the importance of more granular modeling and also indicate mismatches of PV generation and HP load based on hourly simulation datasets. The back-calculation PV sizing algorithm combined with HP and thermal loads presented in this study exhibited robust performance. The results indicate this approach can be used to accelerate the solar electrification of heating and cooling to offset the use of fossil fuels in northern climates.

show abstract

“…The solar PV system sizing is performed using a new design approach proposed in a recent study [40] to calculate the optimal size of PV for supplying the residential thermal load. This new approach relies on the strength of SAM [68] developed by NREL [68] and uses the flexibility of a Python implementation of SAM [105,106] to design and evaluate the performance of a PV system coupled with a load.…”

Section: Solar Pv Sizingmentioning

confidence: 99%

Residential Sizing of Solar Photovoltaic Systems and Heat Pumps for Net Zero Sustainable Thermal Building Energy

Rana,

Jamil,

Asgari

et al. 2024

Computation

Self Cite

View full text Add to dashboard Cite

To enable net zero sustainable thermal building energy, this study develops an open-source thermal house model to couple solar photovoltaic (PV) and heat pumps (HPs) for grid-connected residential housing. The calculation of both space heating and cooling thermal loads and the selection of HP is accomplished with a validated Python model for air-source heat pumps. The capacity of PV required to supply the HPs is calculated using a System Advisor Model integrated Python model. Self-sufficiency and self-consumption of PV and the energy imported/exported to the grid for a case study are provided, which shows that simulations based on the monthly load profile have a significant reduction of 43% for energy sent to/from the grid compared to the detailed hourly simulation and an increase from 30% to 60% for self-consumption and self-sufficiency. These results show the importance of more granular modeling and also indicate mismatches of PV generation and HP load based on hourly simulation datasets. The back-calculation PV sizing algorithm combined with HP and thermal loads presented in this study exhibited robust performance. The results indicate this approach can be used to accelerate the solar electrification of heating and cooling to offset the use of fossil fuels in northern climates.

show abstract

Vertical free-swinging photovoltaic racking energy modeling: A novel approach to agrivoltaics

Cited by 12 publications

References 32 publications

Estimating the Deterministic and Stochastic Levelized Cost of the Energy of Fence-Type Agrivoltaics

Estimating the Deterministic and Stochastic Levelized Cost of the Energy of Fence-Type Agrivoltaics

Residential Sizing of Solar Photovoltaic Systems and Heat Pumps for Net Zero Sustainable Thermal Building Energy

Residential Sizing of Solar Photovoltaic Systems and Heat Pumps for Net Zero Sustainable Thermal Building Energy

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