LAUSR

Combined Heat and Power (CCHP) systems provide electricity, chilled water, and steam for industries and buildings. The choice of the equipment used as well their operation to meet the energy demands depends on a series of factors, among them investment costs, fuel and electricity tariffs, seasonality of loads, and maintenance costs. A challenge faced in the optimization of those systems using Linear Programming is the discrete and non-uniform availability of equipment on the market. Satisfactory results can be obtained using an exhaustive search method coupled with Linear Programming, although this requires a high computational effort. On the other hand, the use of MILP is fast, but the equipment rated power must be uniformly available, with is not realistic. In this paper, we compare two methods for optimizing CCHPs: MILP (Mixed Integer Linear Programming), which used only 0.16% of the original time but with an imprecision of 8%; and a hybrid method of mixing MILP, Selective Search and Linear Programming, which used 4.2% of the original time without loss of precision. The results showed that the hybrid method has the same precision as the exhaustive search method, but with a much shorter processing time.