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Abstract-This paper is concerned with the risk-limiting operation of electric power grids with stochastic uncertainties due to, for example, demand and integration of renewable generation. The main contribution is incorporating autoregressive-moving-average (ARMA) type prediction models for the underlying uncertainties into chance-constrained, finitehorizon optimal control. This uncertainty model leads to a more (compared to existing work in literature) careful treatment of correlation in time which is significant especially in renewable generation yet has attracted limited attention. The paper first discusses how the resulting chance-constrained optimization problems can be solved computationally and demonstrates the effects of the use of the proposed prediction models through simulation-based case studies with realistic data.

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We propose a decentralized electricity dispatch and pricing mechanism that enables a grid with intermittent energy sources to dispatch energy within a user-specified risk bound. This approach allows each power provider to specify the probability density function of the amount of energy that it has to generate in the future. As a result, a power provider can quantitatively limit the risk of power shortage by imposing chance constraints in a decentralized manner. We demonstrate the capabilities of the proposed approach by simulations using real data.