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An outage detection framework for power distribution networks is proposed. Given the tree structure of a distribution system, a detection method is developed combining the use of real-time power flow measurements on the edges of the tree with load forecasts at the nodes of the tree. The maximum likelihood detection decision for an arbitrary number of outages is shown to be efficiently computable due to decoupling across local areas determined by the sensor locations. To minimize the maximum missed detection probability, the optimal sensor placement is efficiently computed as well. Finally, a set of case studies is conducted using feeder data from the Pacific Northwest National Laboratories. It is shown that $10\%$ mean detection error probability can be achieved by a sensor density of $30\%$ for a typical feeder with the proposed optimal sensor placement and outage detection methods. [ABSTRACT FROM PUBLISHER]

Aggregation of diverse wind power sources can effectively reduce their uncertainty, and hence the cost of wind energy integration. A risky power contract is proposed, by which wind power producers (WPPs) can trade uncertain future power for efficient wind aggregation. A two-settlement market with both the risky power contract and a conventional firm power contract is shown to have a unique competitive equilibrium (CE), characterized in closed form. The marginal contribution and diversity contribution of each WPP to the group of all WPPs are fairly reflected in the profit earned by this WPP at the CE. Moreover, the CE achieves the same total profit as achieved by a grand coalition of WPPs. In a coalitional game setting, the profit allocation induced by the CE is always in the core, and is achieved via a non-cooperative risky power market. The benefits of the risky power market are demonstrated using wind generation and locational marginal price data for ten WPPs in the PJM interconnection. [ABSTRACT FROM PUBLISHER]