Assessment of Feed-in Tariff Policy in Thailand: Impacts on National Electricity Prices

Pita, Piti; Tia, Warunee; Suksuntornsiri, Pawinee; Limpitipanich, Paiboon; Limmeechockchai, Bundit

doi:10.1016/j.egypro.2015.11.537

Cited by 8 publications

(5 citation statements)

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“…The retail electricity price (P bg ) represents the average residential electricity costs per unit during 2019 in the metropolitan area [34]. The buyback price (P sg ) represents a Short Run Marginal Cost (SRMC) [12]. The simulated energies are scheduled based on trading periods, decentralized trading in the daytime, and centralized trading at nighttime.…”

Section: Energy Allocation and Price Calculation In Decentralized Ele...mentioning

confidence: 99%

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Decentralized P2P Electricity Trading Model for Thailand

Pinyo

Bangviwat

Menke

et al. 2021

Sensors

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Thailand’s power system has been facing an energy transition due to the increasing amount of Renewable Energy (RE) integration, prosumers with self-consumption, and digitalization-based business models in a Local Energy Market (LEM). This paper introduces a decentralized business model and a possible trading platform for electricity trading in Thailand’s Micro-Grid to deal with the power system transformation. This approach is Hybrid P2P, a market structure in which sellers and buyers negotiate on energy exchanging by themselves called Fully P2P trading or through the algorithm on the market platform called Community-based trading. A combination of Auction Mechanism (AM), Bill Sharing (BS), and Traditional Mechanism (TM) is the decentralized price mechanism proposed for the Community-based trading. The approach is validated through a test case in which, during the daytime, the energy import and export of the community are significantly reduced when 75 consumers and 25 PV rooftop prosumers participate in this decentralized trading model. Furthermore, a comparison analysis confirms that the decentralized business model outperforms a centralized approach on community and individual levels.

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Section: Energy Allocation and Price Calculation In Decentralized Ele...mentioning

confidence: 99%

“…The overall contract capacity is limited, and the incentive price is reduced due to the impact of repurchasing pass-through energy costs on customers' electric bills [9,12]. 4.…”

mentioning

confidence: 99%

Decentralized P2P Electricity Trading Model for Thailand

Pinyo

Bangviwat

Menke

et al. 2021

Sensors

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“…It was also called 'adder' because its adds premium or additional payment to all renewable energy generators on the top of normal prices. In this context, the adder rates mainly depend on energy technology and installed power (Tongsopit and Greacen, 2013;Pita et al, 2015;EPPO, 2010), as depicted in Table 3. …”

Section: Feed-in Tariff (Fit) Mechanism In Thailandmentioning

confidence: 99%

An Assessment of GHGs Reduction Potential from Bio-Electricity Generation: Agricultural Waste-to-Energy Project in Thailand as a Case Study

Kittipongvises

2016

American Journal of Environmental Sciences

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Driven by concerns of energy security and global climate change, this study aimed to investigate the potential of greenhouse gases (GHGs) emissions reduction from bio-electricity project in Thailand. A cogeneration plant in which deploying biomass residues from sugar cane production was selected as a case study. By considering the ACM0006 method, namely "Consolidated methodology for electricity and heat generation from biomass", the findings indicated that the utilization of about 1,320,000 tonnes per year of excess bagasse and 100,000 tonnes per year of rice husk residues could potentially lower the amount of GHGs emissions approximately 102,441.09 tCO 2 e per year. Under this scheme, over 90% of total baseline emissions came from electricity generation by biomass residues. Meanwhile, biomass combustion was considered to be the main source of GHGs emissions compared to other activities. Lack of systematic data collection and cohesion in calculation methods were the key barriers to development of bio-energy project in Thailand.

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“…The calculation for the Ft follows Pita, Tia, Suksuntornsiri, Limpitipanich, and Limmeechockchai (2015) where they divide the subsidy value by the total grid generating capacity. In their paper, the authors examine the subsidy cost side, while this study also offsets the subsidy with the costs saved on producing less electricity with old and new clean coal and gas technology, resulting in the net-subsidy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The models in this study are from Pita et al (2015) and Tongsopit et al (2015). The first paper compares the subsidies in Thailand over time using the previous power development plan.…”

Section: Modelmentioning

confidence: 99%

Regional development models with clean coal power plants

Odermatt

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This thesis examines clean coal technology to be used in Thailand in three studies. The economic impacts on the economic structure of southern Thailand are forecasted with the input-output method. Further, the relative levelized costs of clean coal energy?relative to its amount of carbon equivalent emissions are compared to other technologies and their carbon equivalent emissions in order to compute a carbon certificate price. Lastly, the costs of the solar subsidy on the end consumer is calculated in different scenarios. The results suggest?that there may be slight changes in the economic structure in southern Thailand with increases in the higher technology manufacturing?sector, in the services sector and in repair, trade and construction.?Furthermore, new?clean coal is a sensible choice for stable and cheap energy, at the cost of more pollution compared to other current technologies.?Natural gas is politically not an option, and solar is still quite expensive. Lastly, the solar power subsidy comes at a cost in all scenarios. The limit on?yearly new solar power installations that are covered with the solar subsidy should be replaced with a decrease in the solar power subsidy per energy unit over time. This would increase the total generating capacity by solar power.

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Assessment of Feed-in Tariff Policy in Thailand: Impacts on National Electricity Prices

Cited by 8 publications

References 0 publications

Decentralized P2P Electricity Trading Model for Thailand

Decentralized P2P Electricity Trading Model for Thailand

An Assessment of GHGs Reduction Potential from Bio-Electricity Generation: Agricultural Waste-to-Energy Project in Thailand as a Case Study

Regional development models with clean coal power plants

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