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The complex-valued neural networks are the class of networks that solve complex problems by using complex-valued variables. The gradient descent method is one of the popular algorithms to train complex-valued neural networks. Essentially, the established networks are integer-order models. Compared with classical integer-order models, the built models in terms of fractional calculus possess significant...
Although the unique characteristics of intermittent wind generation have been acknowledged and drastic impacts of sudden wind drops have been experienced, no effective integration approach has been developed. In this paper, without considering transmission capacity constraints for simplicity, aggregated wind generation is modeled as a discrete Markov process with state transition matrices established...
More and more wind generation is introduced to electricity markets. Yet, it is not clear how to conduct auctions with significant stochastic wind generation while avoiding dramatically increasing computational complexity. In this paper, the Markov process concept is innovatively used to model wind generation with transition matrices established based on the historical data. Wind generation is then...
In the current US day-ahead electricity markets, auction is complicated because of the existence of both discrete variables and continuous variables, which implies that there may be no linear prices to support equilibrium. To meet the system demand, the Independent System Operator (ISO) uses lump sum “uplift payments” to make generators follow its schedule. However, these uplift payments are opaque...
In current U.S. deregulated wholesale electricity markets, an auction mechanism that minimizes the total bid cost is used to select bids and their output levels. Energy market clearing prices (MCPs) are derived from the shadow prices associated with the system demand constraints in an economic dispatch process with fixed unit commitment status. Therefore they do not reflect no-load and start-up costs,...
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