Optimum sizing of cogeneration plants by means of a genetic algorithm optimization: A case study

Abstract

In the context of increasing energy consumption, multi-generation systems such as combined heat and power generation (CHP) are attractive to meet the increasingly stringent requirements regarding energy saving in buildings. Hospitals are great consumers of energy, both electrical and thermal: the use of heating and cooling equipment for maintaining satisfactory comfort and indoor air quality for the patients as well as the adoption of several electrical health equipment result in the highest energy consumption per unit floor area of the entire building sector.In the present study, co/tri-generation systems’ optimal set-up, size and operation are investigated for small/medium size hospital facilities. More specifically, after the presentation of the energy consumption profiles for a medium size hospital with 600 beds, set as reference case for this study, a parametric analysis has been carried out varying the peak loads of the user. For each of the proposed scenarios, the optimal plant configuration (sizing of all the energy production systems) has been outlined by means of a numerical code (Trigen 3.0) in-house developed. Afterwards, in order to optimize the load distribution in a smart grid characterized by electrical, thermal, cooling and fuel energy fluxes, an ulterior numerical investigation has been performed. The software, named EGO (Energy Grids Optimizer) consists of a genetic algorithm procedure: it defines the optimal load distribution of a number of energy systems operating into a smart grid based on the minimization of an objective function which expresses the total cost of energy production. Finally, an economic analysis has been carried out in order to evaluate the profitability of the proposed CHP-heat pump scenario. Keywords: Hospital facility, Combined heat and power (CHP), Smart grids, Parametric analysis, Genetic algorithm