Multi-objective Operation Problem Research Articles

GPNBI-inspired MOSFA for Pareto operation optimization of integrated energy system

With the rapid growth of penetration of distributed generations and the strong interdependencies of multiple energy sources, the optimal operation of integrated energy systems (IESs) is becoming more important than ever before. However, current optimal operation strategies for IESs may be impractical because their potential negative impacts on utility grids are generally not considered. Therefore, in this study, an original multiobjective optimization model for IES operation is formulated to minimize the operational cost, primary energy consumption, and carbon dioxide emission of IESs and to optimally reduce the power loss and voltage magnitude deviation of the utility grid. Then, a novel Pareto optimization algorithm, called multiobjective strength firefly algorithm (MOSFA), is proposed to solve the multiobjective operation problem of IES. The strength firefly algorithm (SFA) uses a Boltzmann distribution and a chaotic-sequence-based population selection process to facilitate local exploitation and global exploration. In addition, a generalized piecewise normal boundary intersection (GPNBI) method is developed to transform the multiobjective operation problem into a series of highly constrained single-objective optimization sub-problems that can be effectively solved by the SFA. Finally, a hyper-plane-based decision making strategy is introduced to identify the best compromise solution for the obtained Pareto frontiers. The GPNBI-inspired MOSFA was comprehensively evaluated on a novel IES powered by natural gas, wind and photovoltaic generators. The standard IEEE 39-bus system is considered as the utility grid. The numerical results demonstrated that the proposed MOSFA exhibits competitive performance when compared to the algorithms of the state, which means that the optimized operation strategy provides a better trade-off between all objectives considered in this study.

Multi-objective optimization via fuzzy-evolution method

The purpose of this paper is to address the multi-objective optimization via combined fuzzy satisfied method and evolution programming (E.P.) method. The concept of non-inferiority is employed to characterize a solution of the multi-objective problem. Then, a fuzzy satisfied method based on evolutionary programming is introduce d to determine the optimal solution. As a result, the objective functions of the optimization problem are modeled with fuzzy sets to represent their imprecise natur e. That also enables us to reduce the inaccuracies in decision-makers' judgments. A time-sharing computer program is implemented, and an application to a multi-objective operation problem in feeder reconfiguration in electric power systems is demonstrated along with the computer outputs. In conclusion, the proposed solution algorithm allows for a more realistic problem formulation efficiently obtained the optimal solution for the tested system with a large search space.