We investigate diagnosability of stochastic discrete-event systems where the observation of certain events is unreliable, that is, there are non-zero probabilities of the misdetection and misclassification of events based on faulty sensor readings. Such sensor unreliability is unavoidable in applications such as nuclear energy generation. We propose the notions of uA-and uAA-diagnosability for stochastic automata and demonstrate their relationship with the concepts of A-and AA-diagnosabilty defined in [1]. We extend the concept of the stochastic diagnoser to the unreliable observation paradigm and find conditions for uA-and uAA-diagnosability.

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Hybrid energy systems (HES) have been proposed to be an important element to enable increasing penetration of clean energy. This paper proposes a methodology for operations optimization to maximize their economic value based on predicted renewable generation and market information. A multi-environment computational platform for performing such operations optimization is also developed. To compensate for prediction error, a control strategy is accordingly designed to operate a standby energy storage element (ESE) to avoid energy imbalance within HES. The proposed operations optimizer allows systematic control of energy conversion for maximal economic value. Simulation results of two specific HES configurations illustrate the proposed methodology and computational capability. Economic advantages of such operations optimizer and associated flexible operations are demonstrated by comparing the economic performance of flexible operations with that of constant operations. Sensitivity analysis with respect to market variability and prediction error are also performed.