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This paper presents a concept overview of an automatic operator of electrical networks (AOEN) for real-time alleviation of component overloads and increase of system static load-ability, based on state-estimator data only. The control used for this purpose is real-power generation rescheduling, although any other control input could fit the new framework. The key performance metrics are the vulnerability index of a generation unit (VIGS) and its sensitivity (SVIGS), accurately computed using a realistic ac power flow incorporating the AGC model (AGC-PF). Transmission overloads, vulnerability indices and their sensitivities with respect to generation control are translated into fuzzy-set notations to formulate, transparently, the relationships between incremental line flows and the active power output of each controllable generator. A fuzzy-rule-based system is formed to select the best controllers, their movement and step-size, so as to minimize the overall vulnerability of the generating system while eliminating overflows. The controller performance is illustrated on the IEEE 39-bus (New England) network and the three-area IEEE-RTS96 network subjected to severe line outage contingencies. A key result is that min- imizing the proposed vulnerability metric in real-time results in increased substantial loadability (prevention) in addition to overload elimination (correction). [ABSTRACT FROM AUTHOR]

Efficiently accommodating uncertain renewable resources in wholesale electricity markets is among the foremost priorities of market regulators in the US, UK and EU nations. However, existing deterministic market designs fail to internalize the uncertainty and their scenario-based stochastic extensions are limited in their ability to simultaneously maximize social welfare and guarantee non-confiscatory market outcomes in expectation and per each scenario. This article proposes a chance-constrained stochastic market design, which is capable of producing a robust competitive equilibrium and internalizing uncertainty of the renewable resources in the price formation process. The equilibrium and resulting prices are obtained for different uncertainty assumptions, which requires using either linear (restrictive assumptions) or second-order conic (more general assumptions) duality in the price formation process. The usefulness of the proposed stochastic market design is demonstrated via the case study carried out on the 8-zone ISO New England testbed. [ABSTRACT FROM AUTHOR]