Genetic algorithms and non-intrusive energy management system based economic dispatch for cogeneration units

By integrating neural networks (NNs) with turn-on transient energy analysis, this work attempts to recognize demand load, including the buyers’ load on the power systems and the internal load on the cogeneration systems, thereby increasing the recognition accuracy in a non-intrusive energy management (NIEM) system. Analysis results reveal that an NIEM system and a new method that is based on genetic algorithms (GA) can effectively manage energy demand in an optimal economic dispatch for cogeneration systems with multiple cogenerators, which generate power for buyers. Furthermore, the global optimum of economic dispatch under typical environmental and operating constraints of cogeneration systems is found using the proposed approach, which is based on genetic algorithms. Moreover, the use of the proposed GA-based method for economic dispatch can substantially reduce computational time, fuel cost, power cost and air pollution.