Optimal security-constrained power scheduling by Benders decomposition

This paper presents a Benders decomposition approach to determine the optimal day-ahead power scheduling in a pool-organized power system, taking into account dispatch, network and security constraints. The study model considers the daily market and the technical constraints resolution as two different and consecutive processes. The daily market is solved in a first stage subject to economical criteria exclusively and then, the constraints solution algorithm is applied to this initial dispatch through the redispatching method. The Benders partitioning algorithm is applied to this constraints solution process to obtain an optimal secure power scheduling. The constraints solution includes a full AC network and security model to incorporate voltages magnitudes as they are a critical factor in some real power systems. The algorithm determines the active power committed to each generator so as to minimize the energy redispatch cost subject to dispatch, network and security constraints. The solution also provides the reactive power output of the generators, the value of the transformers taps and the committed voltage control devices. The model has been tested in the IEEE 24-bus Reliability Test System and in an adapted IEEE 118-bus Test System. It is programmed in GAMS mathematical modeling language. Some relevant results are reported.Publicad

Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost expectation while satisfying reliability-related constraints. We propose a distributed scenario-based chance-constrained optimization formulation for this problem. To tackle tractability issues arising in large networks, we use machine learning to build a proxy for predicting outcomes of power system operation processes in this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains cheaper and more reliable plans than other candidates