Two-phase multi-objective evolutionary approaches for optimal generation scheduling with environmental considerations

Abstract

Abstract-This paper presents novel two-phase multi-objective evolutionary approaches for solving the optimal generation scheduling problem with environmental considerations. Two different multi-objective evolutionary algorithms (MOEA) based on Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Archived Multi-objective Simulated Annealing (AMOSA) are presented in the paper. In the first phase, this approach formulates the hourly optimal generation scheduling problem as a multi-objective optimization problem which simultaneously minimizes operation cost and emission, while satisfying constraints such as power balance, spinning reserve and power generation limits. Results of the first phase are compared and SPEA2, which provided better results, is used for the second phase to obtain the optimal schedules for the 24 hours. The minimum up/down time and ramp up/down rate constraints are incorporated in the second phase. A case study for a 10-unit test system is carried out to illustrate the application and the effectiveness of the proposed approach.

Evolutionary Multiobjective Optimization algorithm for multimedia delivery in critical applications through Content-Aware Networks

“…Example parameters defining the content source are the load of the server and its access bandwidth. Other authors ( [18][19][20]) presented two-phase approaches for EMO but, in those cases, the two phases tried to increase efficiency of the algorithm itself. In our case, the two phases are mandatory by the same nature of CAN and the approach to solve the problem is completely different.…”