Title :
Thermal Generation Flexibility With Ramping Costs and Hourly Demand Response in Stochastic Security-Constrained Scheduling of Variable Energy Sources
Author :
Hongyu Wu ; Shahidehpour, Mohammad ; Alabdulwahab, Ahmed ; Abusorrah, Abdullah
Author_Institution :
Power Syst. Eng. Center, Nat. Renewable Energy Lab., Golden, CO, USA
Abstract :
This paper proposes a stochastic day-ahead scheduling of electric power systems with flexible resources for managing the variability of renewable energy sources (RES). The flexible resources include thermal units with up/down ramping capability, energy storage, and hourly demand response (DR). The Monte Carlo simulation (MCS) is used in this paper for simulating random outages of generation units and transmission lines as well as representing hourly forecast errors of loads and RES. Numerical tests are conducted for a 6-bus system and a modified IEEE 118-bus system and the results demonstrate the benefits of applying demand response as a viable option for managing the RES variability in the least-cost stochastic power system operations.
Keywords :
Monte Carlo methods; demand forecasting; numerical analysis; power generation economics; power generation scheduling; power system security; power transmission lines; renewable energy sources; stochastic processes; thermal power stations; 6-bus system; Monte Carlo simulation; down ramping capability; electric power systems; energy storage; generation units; hourly demand response; least-cost stochastic power system operations; modified IEEE 118-bus system; numerical tests; ramping costs; random outage simulation; renewable energy source variability management; stochastic day-ahead scheduling; stochastic security-constrained scheduling; thermal generation flexibility; thermal units; transmission lines; up ramping capability; variable energy sources; Load management; Renewable energy sources; Thermal loading; Time series analysis; Wind forecasting; Wind speed; Demand response; flexible ramping capability; renewable energy sources; stochastic day-ahead scheduling;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2014.2369473