Prediction of Electricity Price Quotation Data of Prioritized Clean Energy Power Generation of Power Plants in The Buyer's Market

Bibliographic Details
Main Author:	Li, Jiasen
Language:	English
Published:	University of Cincinnati / OhioLINK 2021
Subjects:	Computer Science preidiction game theory short term load forecasting svm SVR genetic algorithm
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627663082026476

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spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-ucin16276630820264762021-10-16T05:25:21Z Prediction of Electricity Price Quotation Data of Prioritized Clean Energy Power Generation of Power Plants in The Buyer's Market Li, Jiasen Computer Science preidiction game theory short term load forecasting svm SVR genetic algorithm Electric power is one of the most important energy sources in the world. The stable supply of electric power plays an important role in production development, work, and life. People in all walks of life can do nothing without electricity. Therefore, if the power system is unstable, resulting in the occurrence of power failure, a certain area will be basically paralyzed, the communication will be blocked, the production will not be able to proceed, and the hospital will not be able to treat the patients, people's lives cannot be guaranteed. Therefore, it is very important to ensure the continuous, reliable and stable supply of electricity.In recent years, with the continuous development of global electricity market reform, electricity has become a freely traded commodity, and its price changes in real-time. Therefore, electricity price has become the most concerned issue. Generally, load and power generation are affected by meteorological factors such as temperature, wind speed, and precipitation, as well as the intensity of business and daily activities, such as weekends, hour etc. Therefore, electricity prices show seasonal and highly complex volatility in different time scales (daily, weekly, and annual), and there are often sudden and short-term price spikes.The fluctuation of electricity prices makes it more difficult to predict the behavior of participants in the power market and increases the risk of imbalance between supply and demand in the power market, which affects the stability of power grid operation. In the market-oriented environment of power trade, accurate price forecasting is of great significance to all stakeholders in the power market. As the buyer of the electricity trading market, it is more likely to obtain more profits in electricity trading by obtaining accurate price information in advance. From the perspective of power consumers, such as some factories with large power consumption, electricity price occupies a key part of their production cost. They can make a reasonable power plant according to the accurate price forecast, so as to control the generation cost. Some ordinary power consumers, such as household users, can reduce the cost of living by using household appliances that can automatically control the time to enjoy the electricity price during the low period. From the perspective of the whole power system, power suppliers can rely on accurate electricity price forecast to deal with the generation scheduling of power shortage or excess events in some specific time period, which is conducive to improving the system load rate, reducing the system operation cost, and ensuring the security and stability of the power system.This thesis provides the methods of power load forecasting and wind speed forecasting, as well as the model to simulate the changes of market bidding. Using the results of these forecasts, we bring them into the market bidding model, in the buyer's market, on the premise of predicting the market power consumption and wind power generation in the next stage, making the seller achieve the highest income, while the buyer can purchase electric energy at the lowest purchase price. 2021-10-05 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627663082026476 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627663082026476 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection	NDLTD
language	English
sources	NDLTD
topic	Computer Science preidiction game theory short term load forecasting svm SVR genetic algorithm
spellingShingle	Computer Science preidiction game theory short term load forecasting svm SVR genetic algorithm Li, Jiasen Prediction of Electricity Price Quotation Data of Prioritized Clean Energy Power Generation of Power Plants in The Buyer's Market
author	Li, Jiasen
author_facet	Li, Jiasen
author_sort	Li, Jiasen
title	Prediction of Electricity Price Quotation Data of Prioritized Clean Energy Power Generation of Power Plants in The Buyer's Market
title_short	Prediction of Electricity Price Quotation Data of Prioritized Clean Energy Power Generation of Power Plants in The Buyer's Market
title_full	Prediction of Electricity Price Quotation Data of Prioritized Clean Energy Power Generation of Power Plants in The Buyer's Market
title_fullStr	Prediction of Electricity Price Quotation Data of Prioritized Clean Energy Power Generation of Power Plants in The Buyer's Market
title_full_unstemmed	Prediction of Electricity Price Quotation Data of Prioritized Clean Energy Power Generation of Power Plants in The Buyer's Market
title_sort	prediction of electricity price quotation data of prioritized clean energy power generation of power plants in the buyer's market
publisher	University of Cincinnati / OhioLINK
publishDate	2021
url	http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627663082026476
work_keys_str_mv	AT lijiasen predictionofelectricitypricequotationdataofprioritizedcleanenergypowergenerationofpowerplantsinthebuyersmarket
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Prediction of Electricity Price Quotation Data of Prioritized Clean Energy Power Generation of Power Plants in The Buyer's Market

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