A Study of Incentive Policies for Building-Integrated Photovoltaic Technology in Hong Kong

Song, Aotian; Lu, Lin; Liu, Zhizhao; Wong, Man Sing

doi:10.3390/su8080769

Cited by 21 publications

(15 citation statements)

References 69 publications

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“…Researchers built theories on how incentive policies promote innovation development by analyzing the status quo and existing problems of industry (or industry development). For example, Song et al [20] analyzed the situation and experience of the photovoltaic industry and proposed a series of incentive strategies to help enhance development of the industry. Zou et al [21] analyzed the development of China's green building and found that local economic fundamentals and subsidy-based incentive policies can explain the presence of green buildings.…”

Section: Previous Research On Transmission Mechanisms Of Incentivesmentioning

confidence: 99%

Complex Network Analysis of Transmission Mechanism for Sustainable Incentive Policies

Feng

Guo

et al. 2020

Sustainability

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Existing research mainly focuses on the external impact of incentive policies of industrialized/manufactured construction (IMC). However, it is still unclear how the transmission mechanism among cities and regions of IMC incentive policies works in the process of formulation. To fill the knowledge gap, this study establishes a relationship matrix to propose the transmission-weighted complex network (TWCN). Degree distribution and clustering coefficient are used to calculate the transmission path and the transmission intensity of TWCN. The validation is based on data collected from 415 policy documents (2010–2018) and 2923 items from 181 nodes of the TWCN for IMC policies. The findings show that transmission path of IMC incentive policies is from the eastern coast of China to the central, western and northern regions. Fiscal and taxation incentives have the greatest intensity of spatial agglomeration in the transmission process. The results of the TWCN are consistent and conform to the scientific and rational expectations of research. Overall, the research outcomes are applicable to studies on sustainability policies in different fields, including sustainable construction, renewable energy, etc. Policy makers can implement the TWCN to recognize the performance and functions of different incentives and propose effective strategies to achieve sustainability.

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Section: Previous Research On Transmission Mechanisms Of Incentivesmentioning

confidence: 99%

Complex Network Analysis of Transmission Mechanism for Sustainable Incentive Policies

Feng

Guo

et al. 2020

Sustainability

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“…Namely, the PV industry could be rapidly developed with sufficient financial support from the government, while it could decrease if the government reduced the financial support. In Japan, the government subsidy program in 1994 significantly lowered the PV installation cost, leading to a notable increase in the PV market [30,31]. Then, the PV subsidy was terminated during 2005-2008; accordingly, the installed capacity decreased from 290 MW to 225 MW.…”

Section: Literature Reviewmentioning

confidence: 99%

A Techno-Economic Feasibility Analysis of Mono-Si and Poly-Si Photovoltaic Systems in the Rooftop Area of Commercial Building under the Feed-In Tariff Scheme

Shi

Koo

2021

Sustainability

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Hong Kong’s government has recently introduced the feed-in tariff scheme to promote the photovoltaic (PV) system as a promising way to address global warming. The feed-in tariff scheme depends on the type of the PV system and its installed capacity. This study aimed to investigate the techno-economic feasibility of mono-Si and poly-Si PV systems in the rooftop area of a commercial building, Pao Yue-Kong Library of Hong Kong, under the feed-in tariff scheme. The analysis was carried out in two phases: (i) technical analysis of the rooftop PV systems by considering the shading effect and solar radiation and (ii) economic feasibility of the rooftop PV systems under the feed-in tariff scheme from the life cycle perspective. The main findings of the case study can be summarized: (i) the rooftop area of the target building would not be significantly affected by surrounding buildings; (ii) the highest amount of solar radiation was estimated at 136.96 kWh/m2 in October, while the lowest value was 55.64 kWh/m2 in February; (iii) the total amount of module energy yield from the mono-Si PV system was estimated at 917.58 kWh/kW, indicating that it was very similar but a little bit lower (i.e., 0.48%) than that for the poly-Si PV system (i.e., 921.98 kWh/kW); and (iv) payback periods for mono-Si and poly-Si PV systems were estimated at 8.67 and 8.31 years, respectively. The feasibility study can contribute to providing facility managers with a practical guideline to determine the appropriate strategy in implementing the PV systems in buildings under the feed-in tariff scheme.

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“…In [13] the PV temperature (T PV ) is derived from I o = BT 3 PV e −E g /(kT PV ) , where I o is the is the reverse saturation current of the diode PV model, E g is the band gap of silicon, and B is a temperature-independent constant. Notton el at.…”

Section: Pv Module Performancementioning

confidence: 99%

“…These characteristics have led many countries to pay attention on the design policies, strategies, and technology for its use [1][2][3][4]. In this context, photovoltaic (PV) systems are one of the most mature solar technologies, and it is constantly increasing its competitiveness and decreasing its production costs [5].…”

Section: Introductionmentioning

confidence: 99%

Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System

et al. 2017

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Abstract:Module temperature is an important parameter of photovoltaic energy systems since their performance is affected by its variation. Several cooling controllers require a precise estimation of module temperature to reduce excessive heating and power losses. In this work, an adaptive neuro fuzzy inference system technique is developed for temperature estimation of photovoltaic systems. For the learning process, experimental measurements comprising six environmental variables (temperature, irradiance, wind velocity, wind direction, relative humidity, and atmospheric pressure) and one operational variable (photovoltaic power output) were used as training parameters. The proposed predictive model comprises a zero-order Sugeno neuro fuzzy system with two generalized bell-shaped membership functions per input and 128 fuzzy rules. The model is validated with experimental information from an instrumented photovoltaic system with a fitness correlation parameter of R = 95%. The obtained results indicate that the proposed methodology provides a reliable tool for estimation of modules temperature based on environmental variables. The developed algorithm can be implemented as part of a cooling control system of photovoltaic modules to reduce the efficiency losses.

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A Study of Incentive Policies for Building-Integrated Photovoltaic Technology in Hong Kong

Cited by 21 publications

References 69 publications

Complex Network Analysis of Transmission Mechanism for Sustainable Incentive Policies

Complex Network Analysis of Transmission Mechanism for Sustainable Incentive Policies

A Techno-Economic Feasibility Analysis of Mono-Si and Poly-Si Photovoltaic Systems in the Rooftop Area of Commercial Building under the Feed-In Tariff Scheme

Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System

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