A Hybrid Methodology for a Contingency Constrained Economic Dispatch under High Variability in the Renewable Generation
Keywords:optimal power flow, particle swarm optimization, renewable energy
This work aims at solving the economic dispatch in an electrical network that can be affected by a contingency due to an expected sudden variation in the power produced by the renewable generators. At each bus, the power and voltage levels are determined through a hybrid methodology that combines an evolutionary algorithm with gradient-based methods. Power and voltage assignments aim at minimizing the overall operating cost, subject to constraints of capability margins, acceptable voltage ranges, thermal limits for lines and transformers, and voltage fluctuations. The proposed methodology is based on a master-slave strategy. 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.