A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type

This paper presents a new hybrid algorithm based on the Particle Swarm Optimization (PSO) and the Shuffle Frog Leaping algorithms (SFLA) for solving the Optimal Power Flow (OPF) in power systems. In consequence of economical issues and increasing of the social welfare, the OPF problem is turning into a pretty remarkable problem and getting more and more important in power systems. The proposed optimization problem has considered the real conditions of power generation involving the prohibit zones, valve point effect and multi-fuel type of generation units. Increasing concerns over the environmental issues forced the power system operators to consider the emission problem as a consequential matter beside the economic problems, so the OPF problem has become a multi-objective optimization problem. This paper takes advantages of the Pareto optimal solution and fuzzy decision making method in order to achieve the set of optimal solutions and best compromise solution, respectively. The presented algorithm is applied to 30, 57 and 118-bus test systems and the obtained results are compared with those in literature.

► Proposing a hybrid optimization algorithm (HMPSO-SFLA) to solve Optimal Power Flow (OPF).
► Modifying the PSO algorithm by applying SAPMO for increasing (to enhance or increase) its search ability.
► Considering the generators constraints such as prohibited zones and valve point effect.
►  Considering the multi-fuel for units for closing to real world condition sake.
► Taking emission into account to observe the environmental protection.