Hourly Demand Response in Day-ahead Scheduling for Managing the Variability of Renewable Energy

This paper proposes a stochastic optimization model for the day-ahead scheduling in power systems, which incorporates the hourly demand response (DR) for managing the variability of renewable energy sources (RES). DR considers physical and operating constraints of the hourly demand for economic and reliability responses. The proposed stochastic day-ahead scheduling algorithm considers random outages of system components and forecast errors for hourly loads and RES. The Monte Carlo simulation (MCS) is applied to create stochastic security-constrained unit commitment (SCUC) scenarios for the day-ahead scheduling. A general purpose MILP software is employed to solve the stochastic SCUC problem. Numerical results in the paper demonstrate the benefits of applying DR to the proposed day-ahead scheduling with variable renewable energy sources. Index Terms Hourly demand response, day-ahead scheduling, variable renewable energy sources, load forecast errors, network contingencies, stochastic SCUC. NOMENCLATURE Parameters: T N Number of time periods G N Number of available generators