Environmental and economic performance assessment of integrated conventional solar photovoltaic and agrophotovoltaic systems

Junedi, Mirratul Mukminah; Chachuli, Fairuz Suzana Mohd; Hamid, Nor Aziati Abdul; Purvis-Roberts, Kathleen L.; Jarimi, Hasila; Affandi, Nurfarhana Alyssa Ahmad

doi:10.1016/j.rser.2022.112799

Cited by 17 publications

(4 citation statements)

References 83 publications

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“…In addition, they established that bifacial panels can increase profits by 18 to 35% over monofacial panels [56]. 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: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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

Hwang,

Lee

2024

Energies

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“…Realization of grid parity achievement based on LCOE estimates in small developing states, particularly considering the financing constraints is emphasized for the investment and business perception of RE projects [39]. Impact of the tilt angle and its monthly modification based on a precise assortment is said to decrease the levelised cost of energy, thereby dipping its capital investment's return time [40].Importance of space optimization for residential and agriculture PV energy harvest is learnt through the levelised cost calculation estimated for onshore photovoltaic and agro-photovoltaic [41]. Concept of Grid parity is rebuked for the PV system through the existing Feed in Tariff (FIT) and for the future expansion causes, the fixed investment costs are to be reclaimed by retail tariff reposed by modified additional tariff for the electricity from the grid [42].…”

Section: ) Solar Photovoltaic Generationmentioning

confidence: 99%

A Comprehensive Survey on the Current Trends in Improvising the Renewable Energy Incorporated Global Power System Market

Saranyaa

Fathima

2023

IEEE Access

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Power generation all over the world is slowly being taken over by the renewable energy technologies to alleviate the energy crisis, as well as provide clean and green energy. Therefore, in the foreseeable future, the renewable energy technologies most likely may compete on par level with the conventional energy generation. The near future holds the rebalancing of the global power system market, which mandates, the interpretation of the upcoming and current trends revolving around the power market, pricing and the supply-demand balance with focus on the various renewable energy sources. Levelised Cost of Energy (LCOE) is the present global evaluator that ensures the fair valuation of the generation costs and the grid parity achievement for the various generating technologies. With the power market rebalancing, both regulated and deregulated configuration, have a requisite duty of maintaining the power generation and demand balance. Therefore, this paper does an extensive survey on the recent trends and methodologies in the levelised cost estimation, demand-supply balance with focus on the various renewable generating sources.

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“…Importantly, the power limits employed in a continuous codification are chosen between the maximum and minimum power allowed for the electrical network. These bounds are related to the energetic potential of the Sun in the area where the electrical system is located [9].…”

Section: State-of-the-artmentioning

confidence: 99%

Integration of PV Distributed Generators into Electrical Networks for Investment and Energy Purchase Costs Reduction by Using a Discrete–Continuous Parallel PSO

et al. 2022

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The problem of optimally integrating PV DGs into electrical networks to reduce annual costs (which include energy purchase and investment costs) was addressed in this research by presenting a new solution methodology. For such purpose, we used a Discrete–Continuous Parallel Particle Swarm Optimization method (DCPPSO), which considers both the discrete and continuous variables associated with the location and sizing of DGs in an electrical network and employs a parallel processing tool to reduce processing times. The optimization parameters of the proposed solution methodology were tuned using an external optimization algorithm. To validate the performance of DCPPSO, we employed the 33- and 69-bus test systems and compared it with five other solution methods: the BONMIN solver of the General Algebraic Modeling System (GAMS) and other four discrete–continuous methodologies that have been recently proposed. According to the findings, the DCPPSO produced the best results in terms of quality of the solution, processing time, and repeatability in electrical networks of any size, since it showed a better performance as the size of the electrical system increased.

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Environmental and economic performance assessment of integrated conventional solar photovoltaic and agrophotovoltaic systems

Cited by 17 publications

References 83 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

A Comprehensive Survey on the Current Trends in Improvising the Renewable Energy Incorporated Global Power System Market

Integration of PV Distributed Generators into Electrical Networks for Investment and Energy Purchase Costs Reduction by Using a Discrete–Continuous Parallel PSO

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