Optimization Module Construction Of The Complex Energy System Based On TRNSYS

Complex energy systems are becoming more concentrated as a result of multiplearchitecture energy resource technology’s rapidly development, and their energyefficiency are relied on whether they have scientific optimal strategies or not. Thus, astrategy optimization method is studied in this article by taking advantage of theTRNSYS simulation studio combined with the optimization solving applianceGenOpt.The complex energy system consists of different subsystems, which makes itsoptimization problem more sophisticated. The theory of dynamic programming isused to simplify the problem in this article, and the optimization problem isdecomposed into three procedures: ranking the sequence of subsystem, determiningthe principle of equipment operation, and optimizing the operating data of theequipment. By means of this three-procedure multistage decision, the strategyoptimization possesses an integrated technical roadmap.To abstract a practical engineering problem into a mathematical optimizationproblem, we should get familiar with the optimization problem firstly. Theconstitution, classification and algorithm selection are introduced in this article, andvarious optimization algorithms are presented as well. This introduction providessome reference to the algorithm selection and parameter setting in GenOpt.A complex energy system located in Sino-Singapore Eco-Town No.2powerstation was implemented with the three-procedure roadmap in order to verify thefeasibility of the operation strategy optimization method. Firstly, a ground-source heatpump system-energy storage system-electrical refrigerating system combination wasdetermined in subsystem ranking procedure; fixed variables, independent variablesand intermediate variables were filtered and unique TRNSYS simulation programswere constructed in equipment operation principle determining procedure; optimaloperating data were received by calling external program GenOpt in operating dataoptimizing procedure. Secondly, the operation strategy and the minimum operationcost of a typical diurnal load was analysised. Finally, compared with the practicalenergy consumption, the optimized strategy got a fractional energy saving of32.21%.Through the example, the operation strategy optimization method was verified to be a convenient, accurate and energy-saving effectual method.An association scheme which combining the energy consumption monitoringplatform with the operation strategy optimization method was presented at the lastsection. And the more important point is that this scheme provides a possibility tomake the operation strategy optimization method more productized.