A data-driven analytical approach to enable optimal emerging technologies integration in the co-optimized electricity and ancillary service markets. (1st March 2017)

• Record Type: Journal Article
• Authors: Chen, Yang; Hu, Mengqi; Zhou, Zhi
• Is Part Of: Energy. Volume 122(2017)
• Page Start: 613
• Page End: 626
• Publication Date: 2017-03-01
• Subjects: Data-driven modeling -- Multi-criteria decision -- Electricity and ancillary service market -- Energy storage system -- Demand response -- Co-optimization; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2017.01.102
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The three emerging technologies (renewable energy, energy storage and demand response) play important roles in the co-optimized electricity and ancillary service (EAS) markets where electricity and ancillary service are simultaneously dispatched. While promising, we notice that most literature focuses on either technology integration or operation in the EAS markets. In this research, we develop a three-stage data-driven multi-criteria analytical framework to enable the optimal integration of emerging technologies and operation decisions in an EAS market context under various conditions. We propose multiple performance metrics to evaluate the EAS markets and use a Latin hypercube sampling approach to generate training data for these metrics based on a mixed integer quadratic programming model. Various data-driven models are developed for the performance metrics using the training data and two multi-criteria decision models based on the data-driven models are developed to select optimal technologies based on various criteria. To demonstrate the effectiveness of the proposed framework, we study a revised IEEE 118-bus system. It is demonstrated that our proposed approach can: 1) characterize the relations between each performance metric and technology parameters, 2) determine the significant impact technologies for each performance metric, and 3) recommend optimal emerging technologies integration for market/system operators. Highlights: A data-driven multi-criteria analytical framework is developed to study technology integration. Several quantitative performance metrics are proposed. Several data-driven models are developed to characterize the metrics. Our proposed framework can be used as a powerful tool for market/system operators.