Understanding Deregulated Retail Electricity Markets in the Future: A Perspective from Machine Learning and Optimization

Electricity market;Smart grid;Prosumer;Reinforcement learning;Power system;Machine learning;Electrical and computer engineering;College of Engineering & Computer Science

Chen, TaoRawashdeh, Samir ;

University of Michigan

关键词: Electricity market;    Smart grid;    Prosumer;    Reinforcement learning;    Power system;    Machine learning;    Electrical and computer engineering;    College of Engineering & Computer Science;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/145686/Tao%20Chen%20Final%20Dissertation.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

On top of Smart Grid technologies and new market mechanism design, thefurther deregulation of retail electricity market at distribution level will play a important role in promoting energy system transformation in a socioeconomic way. In today’s retail electricity market, customers have very limited ”energy choice,” or freedom to choose different types of energy services. Although the installation of distributed energy resources (DERs) has become prevalent in many regions, most customers and prosumers who have local energy generation and possible surplus can still only choose to trade with utility companies.They either purchase energy from or sell energy surplus back to the utilities directly while suffering from some price gap. The key to providing more energy trading freedom and open innovation in the retail electricity market is to develop new consumer-centric business models and possibly a localized energy trading platform. This dissertation is exactly pursuing these ideas and proposing a holistic localized electricity retail market to push the next-generation retail electricity market infrastructure to be a level playing field, where all customers have an equal opportunity to actively participate directly. This dissertation also studied and discussed opportunities of many emerging technologies,such as reinforcement learning and deep reinforcement learning, for intelligent energy system operation. Some improvement suggestion of the modeling framework and methodology are included as well.