Modified Honey Bee Mating Optimisation to solve dynamic optimal power flow considering generator constraints

• Published in: IET generation, transmission & distribution Vol. 5; no. 10; pp. 989 - 1002
• Main Authors: NIKNAM, T, NARIMANI, M. R, AGHAEI, J, TABATABAEI, S, NAYERIPOUR, M
• Format: Journal Article
• Language: English
• Published: Stevenage Institution of Engineering and Technology 01.10.2011; The Institution of Engineering & Technology
• Subjects: Applied sciences; Dynamical systems; Electrical engineering. Electrical power engineering; Electrical power engineering; Evolutionary algorithms; Exact sciences and technology; Generators; High voltage or high current generators; Honey; Miscellaneous; Nonlinear dynamics; Nonlinearity; Operation. Load control. Reliability; Optimization; Power flow; Power networks and lines; Various equipment and components; Electric generator; Electrical network analysis; Optimal power flow; Electrical network; Evolutionary algorithm; Non linear function; System simulation; Valve; Nonlinear problems; Comparative study; Search algorithm; Bus system
• ISSN: 1751-8687; 1751-8695
• DOI: 10.1049/iet-gtd.2011.0055

This study proposes a Modified Honey Bee Mating Optimisation (MHBMO) to solve the dynamic optimal power flow (DOPF) problem of power system considering the valve-point effects. DOPF is a complicated non-linear problem that occupies an important role in the economic operation of power system. It has non-smooth and non-convex characteristics when generation unit valve-point effects are taken into account. Non-linear characteristics of the power generators and practical constraints, such as ramp rate constraint, transmission constraints and non-linear cost functions, are all considered for the realistic operation and they cause more complication of the proposed problem. Recently, evolutionary algorithms are devoted to solve compliment problems like the OPF problem. HBMO is one of the evolutionary algorithms considered as a typical swarm-based approach to optimisation, in which the search algorithm is inspired by the process of real honey-bee mating. Besides the privileges of HBMO, it has some drawbacks such as probability of trapped in local optima and converge to global optima in long time.