A rolling-horizon algorithm is proposed for optimizing the operating schedule of a given co-generation energy system while taking into account time-variable loads, tariffs and ambient conditions, as well as financial incentives given on a yearly basis. The algorithm is based on an extension of the Mixed Integer Linear Programming (MILP) model recently developed by the authors for optimizing the daily schedule of cogeneration systems and networks of heat and power plants, involving different co-generation units, boilers, heat pumps and chillers. Heat can be recovered at two temperature levels and can be stored in heat storage tanks; the generated cooling load can be stored as well. 1-D and 2-D piecewise linear approximations are used to deal with the nonlinear performance curves describing the off-design behavior of some units. In this work, first the MILP model is extended to optimize the weekly operation schedule so as to better manage the heat-cold storage systems. In order to account for the Italian incentive policies, which depend on average yearly-basis energy saving indexes, we need to tackle the problem for the whole year. Since the extension of the MILP model from one day to seven days already increases the computational requirements due to the larger number of variables and constraints, we propose a rolling-horizon algorithm in which a sequence of weekly MILP submodels is solved, while considering production and consumption estimates based on demand profiles from historical data. The results obtained for a real-world test case are reported and discussed.