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Leveraging data-driven decision support to enhance flexibility resources adoption in industrial energy systems
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Date
2024
Authors
Siddiquee, S. M. Shahnewaz
Journal Title
Journal ISSN
Volume Title
Publisher
University College Cork
Published Version
Abstract
This thesis presents a comprehensive exploration of decision-support systems aimed at enhancing industrial demand-side participation in energy markets. With the increasing penetration of renewable energy sources, industrial consumers play a vital role in ensuring grid stability through demand-side management (DSM) strategies. The research primarily focuses on data-driven approaches to enhance demand response (DR) participation of industrial consumers, distributed energy resource (DER) selection for industrial sites, and the evaluation of battery energy storage systems (BESS) profitability for industrial consumers. The thesis provides a systematic review of DSM progress in industrial settings, identifying critical barriers and research gaps. These gaps highlight the need for advanced decision-support tools that empower industrial consumers to actively participate in demand response (DR) programs. In response, this thesis proposes a novel data-driven framework that leverages smart meter data and machine learning algorithms to estimate on-site demand-side flexibility. The framework's application in real-world case studies demonstrated an increase in flexibility potential varying from 6%- 24%. A key contribution of this research is the development of a robust multi-criteria decision-making (MCDM) framework designed to assist industrial consumers in selecting the optimal DER configurations. This tool integrates economic, technical, and environmental considerations, enabling more informed decisions tailored to specific industrial needs. The case studies provide a comprehensive implementation of the framework in a real-world scenario. The thesis also introduces a profitability evaluation framework for BESS, focusing on their participation in energy markets. The framework integrates a battery dispatch model with a comprehensive economic evaluation model and a scenario-based sensitivity analysis model. A case study is performed considering an industrial consumer owning and operating BESS in Ireland. The case study results indicate that while energy arbitrage remains financially viable in I-SEM (Integrated Single Electricity Market), the profitability of ancillary services, particularly in smaller markets like DS3(Delivering A Secure Sustainable Power System) program, is limited by current market structures. The findings underscore the necessity for market reforms and enhanced incentives to ensure the broader adoption of BESS technologies in industrial applications. By addressing the key challenges in DSM such as the accurate estimation of demand-side flexibility, the complexity of selecting optimal distributed energy resources, and the financial uncertainties surrounding the deployment of battery energy storage systems, this thesis provides a comprehensive set of decision-support approaches that assists industrial consumers to actively participate in flexibility services. These frameworks not only facilitate better decision-making for flexibility options but also contribute to meet technical, organisational and sustainability goals. Moreover, the adoption of such data-driven approaches enables industrial consumers to transition into proactive energy prosumers, further integrating flexibility resources into their operations and reducing their carbon footprints. Collectively, these advancements hope to pave the way for a more sustainable, efficient, and resilient energy future, where industries can play a pivotal role in balancing energy supply and demand while supporting the global shift toward decarbonization and energy sustainability.
Description
Keywords
Demand side flexibility , Smart grid , Decision support , Demand response , Battery energy storage
Citation
Siddiquee, S. M. S. 2024. Leveraging data-driven decision support to enhance flexibility resources adoption in industrial energy systems. PhD Thesis, University College Cork.