Distributionally robust heat-and-electricity pricing for IES with decision-dependent uncertainties
Optimal Operation of Integrated Energy Systems Under Uncertainties, Page: 63-83
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Book Chapter Description
In this work, we investigate market-pricing problems under endogenous uncertain demands. While traditional pricing considers exogenous energy demands, in multi-energy scenarios, the difference between power and heat prices could affect the compliance of residents to use a certain energy, and therefore affect the realization of stochastic demand. Moreover, the probability distribution of such endogenous uncertainty and how it is influenced by energy prices may not be known exactly. This chapter proposes a bi-level optimization framework based on game theory containing an upper-level dispatch with energy pricing and a series of lower-level household energy-management problems. This framework seeks optimal heat-and-electricity market prices for both IES and users to minimize their expected costs. To address uncertain probability distributions in decision-making processes, a modified distributionally robust chance-constrained programming approach is employed. Results demonstrate that the proposed framework could provide an effective pricing scheme.
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