Operation of Power Grids with High Penetration of Wind Power.

摘要

The integration of wind power into the power grid poses many challenges due to its highly uncertain nature. This dissertation involves two main components related to the operation of power grids with high penetration of wind energy: wind-thermal stochastic dispatch and wind-thermal coordinated bidding in short-term electricity markets. In the first part, a stochastic dispatch (SD) algorithm is proposed that takes into account the stochastic nature of the wind power output. The uncertainty associated with wind power output given the forecast is characterized using conditional probability density functions (CPDF). Several functions are examined to characterize wind uncertainty including Beta, Weibull, Extreme Value, Generalized Extreme Value, and Mixed Gaussian distributions. The unique characteristics of the Mixed Gaussian distribution are then utilized to facilitate the speed of convergence of the SD algorithm. A case study is carried out to evaluate the effectiveness of the proposed algorithm.;Then, the SD algorithm is extended to simultaneously optimize the system operating costs and emissions. A modified multi-objective particle swarm optimization algorithm is suggested to identify the Pareto-optimal solutions defined by the two conflicting objectives. A sensitivity analysis is carried out to study the effect of changing load level and imbalance cost factors on the Pareto front.;In the second part of this dissertation, coordinated trading of wind and thermal energy is proposed to mitigate risks due to those uncertainties. The problem of wind-thermal coordinated trading is formulated as a mixed-integer stochastic linear program. The objective is to obtain the optimal tradeoff bidding strategy that maximizes the total expected profits while controlling trading risks. For risk control, a weighted term of the conditional value at risk (CVaR) is included in the objective function. The CVaR aims to maximize the expected profits of the least profitable scenarios, thus improving trading risk control. A case study comparing coordinated with uncoordinated bidding strategies depending on the trader's risk attitude is included. Simulation results show that coordinated bidding can improve the expected profits while significantly improving the CVaR.