Abstract
This paper presents a methodology for economic optimization of combined cycle district heating systems. Heat and power requirements vary over 24h periods due to changing weather conditions and consumer requirements. System thermal performance is highly dependent on ambient temperature and operating load, because individual component performances are nonlinear functions of these parameters. Since electric grid charges are much higher for on-peak than off-peak periods, on-site fuel choices vary in prices, and cheaper fuel availabilities are limited by suppliers, opportunities arise to optimally schedule system operation, and minimize total daily running cost. For such problems a mixed-integer nonlinear programming formulation is proposed. Limited fuel availability constraints make problem solving difficult using classical techniques such as the branch-and-bound method. As an alternative, a genetic algorithm is proposed in which a genetic search is applied only on integer variables and a gradient search is applied on continuous variables. A comparative study using actual system operation data shows optimal scheduling can reduce total daily running cost by 11% and improve system operating efficiency by 6%.
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