Stochastic Scheduling of Integrated Energy Systems Considering Wind Power and Multienergy Loads Uncertainties

This paper proposes a stochastic day-ahead scheduling framework for integrated energy systems (IES) considering uncertain wind power generation and multienergy loads. The structure and modeling of the IES consisting of electrical, natural gas, and heating networks are presented. The network constraints of the electricity network, natural gas network, and heating network are formulated and incorporated into the optimization model. Uncertainties of hourly wind power and multienergy loads are taken into account by creating multiple representative scenarios with different probabilities using Monte Carlo simulation. The stochastic scheduling model is formulated as a mixed integer linear programming (MILP) problem by linearizing the nonlinear constraints, with the objective function of minimizing the total expected operation cost. Numerical results validate the proposed model and demonstrate the impact of natural gas and heating network on IES operation. (c) 2017 American Society of Civil Engineers.