Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation
For distribution system operators, this work provides a method to handle uncertainty in load forecasting and nonconvex power flow, but it is incremental as it combines existing techniques (GMM, SOCP, ADMM).
The paper proposes a two-step scheduling approach for distribution systems that minimizes day-ahead electricity purchase cost using chance-constrained stochastic optimization and minimizes system loss via distributed optimization, demonstrating validity and effectiveness.
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.