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A fast distributed approach is developed for the market clearing withlarge-scale demand response in electric power networks. In addition toconventional supply bids, demand offers from aggregators serving large numbersof residential smart appliances with different energy constraints areincorporated. Leveraging the Lagrangian relaxation based dual decomposition,the resulting optimization problem is decomposed into separate subproblems, andthen solved in a distributed fashion by the market operator and each aggregatoraided by the end-user smart meters. A disaggregated bundle method is adaptedfor solving the dual problem with a separable structure. Compared with theconventional dual update algorithms, the proposed approach exhibits fasterconvergence speed, which results in reduced communication overhead. Numericalresults corroborate the effectiveness of the novel approach.

Due to its reduced communication overhead and robustness to failures,distributed energy management is of paramount importance in smart grids,especially in microgrids, which feature distributed generation (DG) anddistributed storage (DS). Distributed economic dispatch for a microgrid withhigh renewable energy penetration and demand-side management operating ingrid-connected mode is considered in this paper. To address the intrinsicallystochastic availability of renewable energy sources (RES), a novel powerscheduling approach is introduced. The approach involves the actual renewableenergy as well as the energy traded with the main grid, so that thesupply-demand balance is maintained. The optimal scheduling strategy minimizesthe microgrid net cost, which includes DG and DS costs, utility of dispatchableloads, and worst-case transaction cost stemming from the uncertainty in RES.Leveraging the dual decomposition, the optimization problem formulated issolved in a distributed fashion by the local controllers of DG, DS, anddispatchable loads. Numerical results are reported to corroborate theeffectiveness of the novel approach.

High wind energy penetration critically challenges the economic dispatch ofcurrent and future power systems. Supply and demand must be balanced at everybus of the grid, while respecting transmission line ratings and accounting forthe stochastic nature of renewable energy sources. Aligned to that goal, anetwork-constrained economic dispatch is developed in this paper. To accountfor the uncertainty of renewable energy forecasts, wind farm schedules aredetermined so that they can be delivered over the transmission network with aprescribed probability. Given that the distribution of wind power forecasts israrely known, and/or uncertainties may yield non-convex feasible sets for thepower schedules, a scenario approximation technique using Monte Carlo samplingis pursued. Upon utilizing the structure of the DC optimal power flow (OPF), adistribution-free convex problem formulation is derived whose complexity scaleswell with the wind forecast sample size. The efficacy of this novel approach isevaluated over the IEEE 30-bus power grid benchmark after including realoperation data from seven wind farms.