Fondecyt 1231892
Addressing the operational flexibility needs of power systems through the integration of emerging technologies in electricity markets
Abstract
Electricity systems and markets are evolving. Rapid deployment of low-carbon technologies and smart grids, together with electrification of the energy matrix and a more active role of consumers, are challenging existing technological, economic, and regulatory paradigms. As net load variability and uncertainty caused by variable renewable energy (VRE) sources keeps increasing, power systems require more flexible resources (e.g. flexible generation, energy storage, or responsive demand) to balance the load on different time scales. Hence, power system operators, planners, and regulators are being driven to create market designs to fully embrace the potential benefits of disruptive greener technologies while ensuring power system reliability, economic efficiency, incentive compatibility, and fairness. Misallocation of incentives in low-carbon power systems may lead to a mix of resources unable to satisfy the underlying system's needs, concealing the true value of storage and flexible generation and demand. Hence, operational flexibility requirements necessitate special market designs capable of promoting innovation and deployment of new technologies.
All these drivers are encouraging the development of balancing ancillary service products capable of optimally matching generation and demand while providing economic incentives for innovating and reducing the costs for reliability. However, it is challenging to understand beforehand how a change in the market rules may affect power system operations or to measure the value of flexibility to final consumers. Furthermore, the Chilean energy market is cost-based while some of its frequency control ancillary services are bid-based, making it difficult to extrapolate the lessons learned in other markets directly. Moreover, many authors have highlighted the potential of different emerging technologies to provide additional operational flexibility. Still, it is not always clear how they could participate in the provision of different ancillary services. These aspects posit several research challenges in terms of market design, mathematical modeling, and computer simulation of operational flexibility.
The main goal of this research is to test how different technologies can provide ancillary services for accommodating increasing amounts of variable renewable resources and fostering technological innovation in the Chilean electricity sector. The contributions of this work will be in two areas: (1) Computational modeling of operational flexibility and (2) Market design. The work will be carried out by looping through a three-stage cycle: theoretical work; model design and development; and computational experiments.
Expected outcomes of this research are: (1) Identify future operational flexibility needs and potential ancillary service products and technologies capable of satisfying them; (2) A suite of static and dynamic models and methods for analyzing different designs for ancillary service products and their provision by distinct technologies; (3) Publicly available techno-economic market models of the Chilean interconnected system for studying operational flexibility; (4) Definition and evaluation of different sets of market rules (from the point of view of operational flexibility) for the procurement, operation, remuneration, and payment for different ancillary service products. The open-source availability of tools and methods generated during the project execution should have a significant impact on stimulating and encouraging further research and education in this area.
Journal papers
Tomás Ochoa, Esteban Gil, Alejandro Angulo, Carlos Valle, "Multi-agent Deep Reinforcement Learning for Efficient Multi-Timescale Bidding of a Hybrid Power Plant in Day-Ahead and Real-Time Markets". Applied Energy, vol. 317, July 2022.
DOI: 10.1016/j.apenergy.2022.119067.
Conference papers
Tomás Ochoa, Esteban Gil, Alejandro Angulo, "Efficient Bidding of a PV Power Plant with Energy Storage Participating in Day-Ahead and Real-Time Markets Using Artificial Neural Networks", 2022 IEEE Power & Energy Society General Meeting (IEEE-PES-GM 2022), Denver, USA, July 17-21, 2022.
DOI: 10.1109/PESGM48719.2022.9916732.
Link
NOTE: Some of these papers may be prior to the commencement of the project.