نبذة مختصرة : In this work, we propose a novel multi-scale bottom-up optimization framework for the carbon-neutral transition planning of the electric power sector, which incorporates hourly time scale and electricity storage to address the reliability and energy balance issues of the future deep-decarbonized power systems. In addition to the technology and capacity information for each facility, the proposed framework also accounts for facility ages, which are usually omitted in the literature, without significantly increasing the computational demand. To reduce the computational requirement of simultaneously optimizing capacity planning and hourly systems operations over the next few decades, a reduced model is developed based on representative days, using a novel approach that integrates multiple machine learning techniques. Based on the optimal transition pathways, hourly operational simulations are conducted for every year within the planning horizon to obtain detailed optimization results. To illustrate the applicability of the proposed framework, a case study for the New York State is presented through two cases, with and without electricity storage capacity expansion. The proposed approach using principal component analysis coupled with K-means outcompetes multiple conventional approaches of using clustering techniques directly. The transition planning results show that the total generation capacity for the case with electricity capacity expansion is 39% higher than the other case, while the latter case has 200% more generation capacity from non-intermittent sources. Detailed hourly operational simulation results indicate that offshore wind, hydro, and utility solar are the primary power sources by 2040 for the case with electricity storage capacity expansion, while hydro, offshore wind, and nuclear are the main electricity sources for the other case.
Processing Request
No Comments.