Abstract
This study analyzes the factors leading to the deployment of Power-to-Hydrogen (PtH2) within the optimal design of district-scale Multi-Energy Systems (MES). To this end, we utilize an optimization framework based on a mixed integer linear program that selects, sizes, and operates technologies in the MES to satisfy electric and thermal demands, while minimizing annual costs and CO2 emissions. We conduct a comprehensive uncertainty analysis that encompasses the entire set of technology (e.g. cost, efficiency, lifetime) and context (e.g. economic, policy, grid carbon footprint) input parameters, as well as various climate-referenced districts (e.g. environmental data and energy demands) at a European-scope.Minimum-emissions MES, with large amounts of renewable energy generation and high ratios of seasonal thermal-to-electrical demand, optimally achieve zero operational CO2 emissions by utilizing PtH2 seasonally to offset the long-term mismatch between renewable generation and energy demand. PtH2 is only used to abate the last 5–10% emissions, and it is installed along with a large battery capacity to maximize renewable self-consumption and completely electrify thermal demand with heat pumps and fuel cells. However, this incurs additional cost. Additionally, we show that ‘traditional’ MES comprised of renewables and short-term energy storage are able to decrease emissions by 90% with manageable cost increases.The impact of uncertainty on the optimal system design reveals that the most influential parameter for PtH2 implementation is (1) heat pump efficiency as it is the main competitor in providing renewable-powered heat in winter. Further, battery (2) capital cost and (3) lifetime prove to be significant as the competing electrical energy storage technology. In the face of policy uncertainties, a CO2 tax shows large potential to reduce emissions in district MES without cost implications. The results illustrate the importance of capturing the dynamics and uncertainties of short- and long-term energy storage technologies for assessing cost and CO2 emissions in optimal MES designs over districts with different geographical scopes.
Highlights
Since the turn of the millennium, the energy sector has been experiencing two opposing transformations driven by the need to reduce CO2 emissions while maintaining reliability [1]
The main novelty of this contribution is that the whole set of uncertain economic, technology, and context input parameters and various climate-referenced districts are considered to answer the question: which factors lead to the inclusion of PtH2 to the optimal configuration of district Multi-Energy Systems (MES)? To answer this research question, we evaluate four European reference districts within the framework of a mixed-integer linear program (MILP) optimization model with several objective cases ranging from minimum-cost to minimum-emissions, along with testing the impact of an uncertain CO2 tax and a CO2 emissions cap on the minimum-cost objective
This work presents an optimization framework accounting for factors such as various district conditions and MES techno-economic parameter uncertainty in order to provide a comprehensive answer to the question – which factors lead to the inclusion of PtH2 to the optimal configuration of district MES? We answer this question by utilizing a MILP optimization framework which effectively integrates multiple renewable energy generation, conversion, along with short- and longterm energy storage technologies, utilizing PtH2 as the seasonal energy storage option
Summary
Since the turn of the millennium, the energy sector has been experiencing two opposing transformations driven by the need to reduce CO2 emissions while maintaining reliability [1]. Gabrielli et al studied the impact of uncertain environmental conditions and energy demands on robustness and optimality for several future scenarios of a district MES, but did not take into account other uncertainties [13] To fill these gaps, this contribution (i) presents a framework to characterize and analyze the uncertainty associated with the optimal design of MES with short- and long-term forms of energy storage; (ii) provides a comprehensive UC encompassing all relevant technology and context parameters; (iii) determines the intrinsic system features for which the underlying MES, and PtH2, become viable from an economic and environmental perspective.
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