Abstract: The present invention provides a method and a system for train periodic message scheduling based on a multi-objective evolutionary algorithm, relating to the field of information and communications technology, mainly including: acquiring an MVB periodic message table; binary encoding the MVB periodic message table and initializing it randomly, to generate an iterative population; performing crossover and mutation operations on the individuals of the iterative population using a genetic algorithm, to update the iterative population; constructing an MVB periodic scheduling table that meets scheduling needs and minimizes the macro cycle according to a multi-objective algorithm and the updated iterative population; scheduling train periodic messages according to the MVB periodic scheduling table, thereby meeting the real-time requirement of periodic data transmission in actual scheduling scenarios.

Abstract: The present disclosure discloses a method for optimizing equipment capacity and equipment power of an energy hub system. The method includes establishing an energy hub model containing natural gas boilers, electric boilers, coolers and heat pumps, establishing a bilevel optimized upper model to solve the optimal heat pump capacity, and establishing a bilevel optimized lower model to solve the optimal power utilization of each energy device based on the binary search algorithm of the quadratic function solves the upper model by using the multi-objective evolutionary algorithm NSGA-II to solve the lower model. The optimization method of the present invention can solve the multi-objective bilevel model problem without the help of commercial optimization software. Obtaining a reasonable, efficient and green planning scheme makes the total operating cost and total exhaust gas emissions of the energy hub relatively optimal.

Abstract: The present disclosure discloses a method for optimizing equipment capacity and equipment power of an energy hub system. The method includes establishing an energy hub model containing natural gas boilers, electric boilers, coolers and heat pumps, establishing a bilevel optimized upper model to solve the optimal heat pump capacity, and establishing a bilevel optimized lower model to solve the optimal power utilization of each energy device based on the binary search algorithm of the quadratic function solves the upper model by using the multi-objective evolutionary algorithm NSGA-II to solve the lower model. The optimization method of the present invention can solve the multi-objective bilevel model problem without the help of commercial optimization software. Obtaining a reasonable, efficient and green planning scheme makes the total operating cost and total exhaust gas emissions of the energy hub relatively optimal.

Abstract: The present invention provides a method and a system for train periodic message scheduling based on a multi-objective evolutionary algorithm, relating to the field of information and communications technology, mainly including: acquiring an MVB periodic message table; binary encoding the MVB periodic message table and initializing it randomly, to generate an iterative population; performing crossover and mutation operations on the individuals of the iterative population using a genetic algorithm, to update the iterative population; constructing an MVB periodic scheduling table that meets scheduling needs and minimizes the macro cycle according to a multi-objective algorithm and the updated iterative population; scheduling train periodic messages according to the MVB periodic scheduling table, thereby meeting the real-time requirement of periodic data transmission in actual scheduling scenarios.