Research on hydropower station optimal scheduling considering ecological water demand

Considering that the standard particle swarm optimization has slow convergence speed and is easy to trap into local optimal solution, this paper proposed an improved algorithm with a dynamic neighborhood topology, where the connections between the particles are adjusted with a changing dynamical neighborhood structure of the particle. In the early stage of the algorithm, the impact of the optimal particle is weakened to maintain the diversity of the population and to prevent the algorithm from local optimum, then connections between particles are added to make the algorithm have more rapid convergence in the later stage. Focusing on hydropower optimal scheduling problems, we discussed relevant technologies, built the model of scheduling considering ecological water demand and studied the calculation of river Ecological Water Demand in the ecological operation of hydropower station. We combined ecological operation and generation scheduling taking maximum of power generation as the objective and taking into account constraints like ecological factors of the river, the balance of reservoir water, discharge volume restrictions, output restrictions etc., then we used an improved particle swarm algorithm to solve the optimization problem. The simulation scheduling results show that the algorithm has strong global search ability and rapid convergence speed, which can effectively solve such a multi-constrains, non-linearity problem in hydropower stations scheduling.