PSO algorithm‐based scenario reduction method for stochastic unit commitment problem

Abstract

This paper proposes a particle swarm optimization (PSO) algorithm-based scenario reduction method for stochastic unit commitment problems. In this method, the position of each particle is an index set of the preserved scenarios, that is, a possible solution to the optimal scenario reduction problem. The Kantorovich distance between the original scenarios and the preserved scenarios is used to calculate the fitness value of each particle. A repair procedure is carried out to ensure that there are nonrepeating index numbers in the newly generated position of each particle during the iterations. The performance of the PSO-based method is tested on two scenario sets of electricity prices in a stochastic profit/price-based unit commitment (SPBUC) problem, and is compared with backward reduction and forward selection. Test results show that the PSO-based method performs very well with respect to the relative accuracy and running times when reducing large scenario set. Impacts of scenario reduction on the expected profits of the SPBUC problem are also investigated with different numbers of the preserved scenarios of electricity prices, which are obtained by these three different reduction methods from the same original scenario set. Simulation results show that optimal solutions of the SPBUC problem are related not only to the number of the preserved scenarios but also to the scenario reduction methods. The PSO-based method can lead to less conservative solutions than the forward selection, while the backward reduction can result in nonconservative solutions.

Scenario Reduction for Stochastic Optimization Applied to Short-Term Trading of PV Power

“…Due to its importance, scenario reduction is applied in the most diverse areas of knowledge, namely in supply chain [7] and particularly in the scope of this research, in electricity markets [4]. In power systems, most applications of scenario reduction methods focus on unit commitment and short-term operation [8][9][10]. This paper addresses the scenario reduction in a two-stage stochastic optimization applied to the short-term trading of PV power in a DAM, reformulated as a mixed-integer linear programming approach.…”

Fast Scenario Reduction for Power Systems by Deep Learning

“…But it is still not fast enough for big scenario set. In addition to backward and forward methods, some papers use PSO (Particle swarm optimization) to obtain the optimal reduced scenario set [7,8]. PSO method has similar performance as the FFS method according to [9].…”