Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation

TL;DR

This paper presents a two-step stochastic optimization approach for day-ahead hourly scheduling in distribution systems, integrating chance constraints, Gaussian mixture models, and distributed optimization to improve operational efficiency.

Contribution

It introduces a novel two-stage method combining chance-constrained forecasting with SOCP relaxation and ADMM-based distributed optimization for distribution system scheduling.

Findings

Effective reduction in power purchase costs.

Improved system loss minimization.

Validated approach through simulation results.

Abstract

This paper aims to propose a two-step approach for day-ahead hourly scheduling in a distribution system operation, which contains two operation costs, the operation cost at substation level and feeder level. In the first step, the objective is to minimize the electric power purchase from the day-ahead market with the stochastic optimization. The historical data of day-ahead hourly electric power consumption is used to provide the forecast results with the forecasting error, which is presented by a chance constraint and formulated into a deterministic form by Gaussian mixture model (GMM). In the second step, the objective is to minimize the system loss. Considering the nonconvexity of the three-phase balanced AC optimal power flow problem in distribution systems, the second-order cone program (SOCP) is used to relax the problem. Then, a distributed optimization approach is built based on the alternating direction method of multiplier (ADMM). The results shows that the validity and effectiveness method.