Cost and profit optimization of integrated wind-thermal system by dynamic dispatch using swarm intelligence

Particle swarm optimization (PSO) algorithm is applied to find optimal dispatch for minimizing cost and maximizing profit of conventional units integrated with renewable power. Non-conventional energy sources, such as wind, are highly uncertain in nature. Generation of wind power depends on the wind speed. The uncertainty of wind power in cost model is considered by taking a dynamic dispatch model. This paper considers six conventional thermal generator units taken from IEEE 118-bus test system with wind turbines. The model includes all practical constraints like ramp rate limits and valve point loading effects of thermal generating units. Min-max limits are modified by ramp rate limits of generating systems. Optimization problem includes practical constraints with nonlinearity and non-convexity. For convex cost case, the results are validated using the sequential quadratic programming (SQP) algorithm but unfortunately, the conventional gradient based optimization methods are unsuitable for non-convex cases. On the other hand the swarm intelligence based models guarantee stable convergence to near best solution for such cases.