  Insufficient flexibility in system operation caused by traditional "heat-set"
operating modes of combined heat and power (CHP) units in winter heating
periods is a key issue that limits renewable energy consumption. In order to
reduce the curtailment of renewable energy resources through improving the
operational flexibility, a novel optimal scheduling model based on
chance-constrained programming (CCP), aiming at minimizing the lowest
generation cost, is proposed for a small-scale integrated energy system (IES)
with CHP units, thermal power units, renewable generations and representative
auxiliary equipments. In this model, due to the uncertainties of renewable
generations including wind turbines and photovoltaic units, the probabilistic
spinning reserves are supplied in the form of chance-constrained; from the
perspective of user experience, a heating load model is built with
consideration of heat comfort and inertia in buildings. To solve the model, a
solution approach based on sequence operation theory (SOT) is developed, where
the original CCP-based scheduling model is tackled into a solvable
mixed-integer linear programming (MILP) formulation by converting a chance
constraint into its deterministic equivalence class, and thereby is solved via
the CPLEX solver. The simulation results on the modified IEEE 30-bus system
demonstrate that the presented method manages to improve operational
flexibility of the IES with uncertain renewable generations by comprehensively
leveraging thermal inertia of buildings and different kinds of auxiliary
equipments, which provides a fundamental way for promoting renewable energy
consumption.
