We study supply function competition among conventional power generators with different levels of flexibility and the impact of intermittent renewable power generation on the competition. Inflexible generators commit production before uncertainties are realized, whereas flexible generators can adjust their production after uncertainties are realized. Both types of generators compete in an electricity market by submitting supply functions to a system operator, who solves a two-stage stochastic program to determine the production level for each generator and the corresponding market prices. We aim to gain an understanding of how conventional generators' (in)flexibility and renewable energy's intermittency affect the supply function competition and the market price. We find that the classic supply function equilibrium model overestimates the intensity of the market competition, and even more so when more intermittent generation is introduced into the system. The policy of economically curtailing intermittent generation intensifies the market competition, reduces price volatility, and improves the system's overall efficiency. Furthermore, these benefits of economic curtailment are most significant when the production-based subsidies for renewable energy are absent.
The well-instrumented process industry collects vast amounts of structured and unstructured data from its assets in real time. Some of this data gets stored as conventional time series data, while some is processed to generate alarms, alerts, and other types of unstructured data. Managing this big data which is rich in diversity, volume, veracity, and velocity, to generate actionable insights is a challenge that is best tackled through the use of advanced analytics. The area of advanced analytics has been expanding with the rapid rise of artificial intelligence (AI) tools that are capable of processing complex data types such as video and audio in real time. In this article, applications involving operational data and advanced analytics tools that are used to generate predictive insights are discussed. The case studies illustrate the different data types present in industrytime series data, alarm and event data, and image data and the machine-learning methods used to analyze them in order to generate insights. The applications discussed cover a spectrum of advanced analytics techniques ranging from conventional time series analysis, spectral analysis, clustering, convolutional neural networks, to text analytics. In conclusion, some perspectives on the future role of advanced analytics and AI technologies in the process industry are shared.
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