Two-Timescale Stochastic Dispatch of Smart Distribution Grids

TL;DR

This paper proposes two novel data-driven two-timescale stochastic dispatch schemes for smart distribution grids, enabling efficient renewable integration and voltage regulation by jointly optimizing slow and fast control decisions.

Contribution

It introduces two distribution-free algorithms for two-stage stochastic dispatch, with proven convergence and improved cost efficiency over existing methods.

Findings

Both algorithms converge to optimal decisions.

Numerical tests show lower costs compared to alternatives.

The methods effectively handle stochastic renewable resources.

Abstract

Smart distribution grids should efficiently integrate stochastic renewable resources while effecting voltage regulation. The design of energy management schemes is challenging, one of the reasons being that energy management is a multistage problem where decisions are not all made at the same timescale and must account for the variability during real-time operation. The joint dispatch of slow- and fast-timescale controls in a smart distribution grid is considered here. The substation voltage, the energy exchanged with a main grid, and the generation schedules for small diesel generators have to be decided on a slow timescale; whereas optimal photovoltaic inverter setpoints are found on a more frequent basis. While inverter and looser voltage regulation limits are imposed at all times, tighter bus voltage constraints are enforced on the average or in probability, thus enabling more efficient renewable integration. Upon reformulating the two-stage grid dispatch as a stochastic convex-concave problem, two distribution-free schemes are put forth. An average dispatch algorithm converges provably to the optimal two-stage decisions via a sequence of convex quadratic programs. Its non-convex probabilistic alternative entails solving two slightly different convex problems and is numerically shown to converge. Numerical tests on a real-world distribution feeder verify that both novel data-driven schemes yield lower costs over competing alternatives.