A Hybrid Methodology for a Contingency Constrained Economic Dispatch under High Variability in the Renewable Generation

Abstract

The increasing use of distributed energy resources in power grids has renewed the interest in economic dispatch problems. In this context, a challenging problem consists in minimizing the effects of contingencies due to unwanted sudden variations in the power produced by renewable generators. This work aims at determining the power and voltage assignments for minimizing the overall operating cost, considering a contingency scenario, subject to constraints of capability margins, acceptable voltage ranges, thermal limits for lines and transformers, and voltage fluctuations. To this effect, a hybrid master-slave strategy is proposed. At the master stage, a particle swarm optimization algorithm is utilized to define the injected renewable-based powers; while the slave stage uses an interior-point method to minimize the production costs. After a given contingency, the power flow is evaluated through a Newton-Raphson method. As an application example, the methodology is used to solve the IEEE 30-bus test system, with the addition of several photovoltaic parks and distributed loads with typical demand profiles. The tests proved the effectiveness of the algorithm to solve the problem, and its value as a potential planning tool.