Abstract
The increase in ambient particulate matter (PM) is affecting not only our daily life but also various industries. To cope with the issue of PM, which has been detrimental to the population of megacities, an advanced demand response (DR) program is established by Korea Power Exchange (KPX) to supplement existing policies in Korea. Ironically, however, DR programs have been launched hurriedly, creating problems for several stakeholders such as local governments, market operators, and DR customers. As an alternative, a method for predicting and categorizing the PM through deep learning and fuzzy inference is suggested in this study. The simulation results based on Seoul data show that the proposed model can overcome the problems related to current DR programs and policy loopholes and can provide improvements for some stakeholders. However, the proposed model also has some limitations, which require an in-depth policy consideration or an incentive system for power generation companies.
Highlights
A high content of ambient particulate matter (PM), classified as a Group 1 carcinogen by the WorldHealth Organization, has caused several problems in countries such as China, India, and Korea [1,2,3,4,5].PM harms the human body, and affects our daily life and the industrial sector
As the Korean electricity market is operated by a cost-based pool, to compensate for the amount of reduction in the output of coal-fired power plants, which run on a base-load generator, a peak generator is used, resulting in a high wholesale system marginal price (SMP)
We propose a methodology for predicting the ambient PM through deep learning and fuzzy inference to expand the particulate matter demand response (PMDR) program
Summary
A high content of ambient particulate matter (PM), classified as a Group 1 carcinogen by the World. National efforts are required to alleviate the PM problem and significant resources are being invested for air purification Another representative example is Korea, which is geographically very close to China. Government has implemented a policy to limit the output of coal-fired power generators on days when the fine dust concentration is expected to be at a bad level. As the Korean electricity market is operated by a cost-based pool, to compensate for the amount of reduction in the output of coal-fired power plants, which run on a base-load generator, a peak generator is used, resulting in a high wholesale system marginal price (SMP). A novel model for PM prediction based on an ANN and regulations on the output limits of coal-fired power generation using fuzzy inference. Proposes a direction for policy reference for improving the DR market
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