A Robust Unit Commitment Algorithm for Hydro-Thermal Optimization

This paper presents a unit commitment algorithm which combines the Lagrangian relaxation (LR), sequential unit commitment (SUC), and optimal unit decommitment (UD) methods to solve a general hydro-thermal optimization (HTO) problem. The authors argue that this approach retains the advantages of the LR method while addressing the method\u27\u27s observed weaknesses to improve overall algorithm performance and quality of solution. The proposed approach has been implemented in a version of PG&E\u27\u27s HTO program, and test results are presented

Unit commitment for systems with significant wind penetration

The stochastic nature of wind alters the unit commitment and dispatch problem. By accounting for this uncertainty when scheduling the system, more robust schedules are produced, which should, on average, reduce expected costs. In this paper, the effects of stochastic wind and load on the unit commitment and dispatch of power systems with high levels of wind power are examined. By comparing the costs, planned operation and performance of the schedules produced, it is shown that stochastic optimization results in less costly, of the order of 0.25%, and better performing schedules than deterministic optimization. The impact of planning the system more frequently to account for updated wind and load forecasts is then examined. More frequent planning means more up to date forecasts are used, which reduces the need for reserve and increases performance of the schedules. It is shown that mid merit and peaking units and the interconnection are the most affected parts of the system where uncertainty of wind is concernedpower generation dispatch; power system economics; stochastic systems; wind power generation