Operation optimization of an industrial cogeneration system by a genetic algorithm

Large process plants need energy in several forms (mechanical energy, electricity, steam, hot water etc.), which very often come from a variety of sources such as gas-turbine generators, steam-turbine generators, exhaust gas boilers, fuel-burning boilers etc. In addition, the utility network serves as a source of supplementary electricity if needed, or as a sink when excess electricity is produced. The cost of energy is one of the major contributors to the total operating cost of a process plant. Consequently, minimization of this cost is of utmost importance. Due to the variety of energy sources, the interdependency between sources and the variation of technical and economic conditions with time (e.g. change of load, deterioration of equipment, change of fuel and electricity prices etc.), the task of minimizing energy cost is far from trivial. Methods and algorithms to solve these types of problems are still a subject of research because of the following reasons: the problems are usually nonlinear with multimodal objective functions that may contain both discrete (e.g. integer) and continuous variables. No single method has been successful with every problem of this type. In the present work, a genetic algorithm (GA) is applied for the operation optimization of a cogeneration system, which supplies a process plant with electricity and steam at various pressure levels. A mathematical simulation model of the system has been developed, taking into consideration the real condition of main equipment, as it is revealed by an appropriate set of measurements. The GA is combined with the simulation model, in order to solve the optimization problem under specified constraints. (C) 1997 Elsevier Science Ltd.