Data-Driven Distributionally Robust Operation of Distribution Networks with Ramping Flexibility

The increase in the generation capacity of the variable renewable resources and electricity demand introduces new operational challenges to the unbalanced three-phase distribution networks. This paper addresses the uncertainty associated with the ramping of net demand using a data-driven approach. A continuous-time optimization problem is reformulated to a linear programming problem using Bernstein polynomials. A distributionally robust optimization problem is formulated to capture the worst-case probability distribution of the net demand, which includes the demand and the PV generation. The solution to the distributionally robust operation of the unbalanced distribution network is compared to that of the stochastic programming problem in which the uncertainty associated with the net demand ramp is captured using scenarios. The developed formulated problem is validated using a modified IEEE 13-bus unbalanced distribution system. The impact of ramp limits of the main feeder on the expected operation cost of the distribution network is investigated.