Optimal power flow with transient stability constraints (TSCOPF) becomes an effective tool of many problems in power systems since it simultaneously considers economy and dynamic stability of power system. TSC-OPF is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. This paper presents a novel and efficient optimisation approach named the teaching learning based optimisation (TLBO) for solving the TSCOPF problem. The quality and usefulness of the proposed algorithm is demonstrated through its application to four standard test systems namely, IEEE 30-bus system, IEEE 118-bus system, WSCC 3-generator 9-bus system and New England 10-generator 39-bus system. To demonstrate the applicability and validity of the proposed method, the results obtained from the proposed algorithm are compared with those obtained from other algorithms available in the literature. The experimental results show that the proposed TLBO approach is comparatively capable of obtaining higher quality solution and faster computational time.
Top1 Introduction
Optimal power flow (OPF) (Dommel & Tinney, 1968; Chen, Tada, Okamoto, Tanabe, & Ono, 2001; El-Hawari, 1996; Laufenberg & Pai, 1998; Yan, Xiang, & Zhang, 1996). has become an important issue to the researchers over past two decades and has established its position as one of the main tools for optimal operation and planning of modern power systems. The prime objective of the OPF problem is to optimize generation cost, by optimal adjusting the power system control variables and satisfying various system operating such as power flow equations and inequality constraints, simultaneously
OPF with transient stability constraints is an extension of the traditional OPF problems. In addition to the common constraints of OPF, the TSCOPF problems consider the dynamic stability constraints of power system. When any of a specified set of disturbances occurs, a feasible operation point should withstand the fault and ensure that the power system moves to a new stable equilibrium after the clearance of the fault without violating equality and inequality constraints even during transient period. These conditions for all of the specified credible contingencies are called as transient stability constraints.
Because of larger power transfers over longer distances, complex coordination, difficult interaction among various system controllers and less power reserves, complexity is gradually increasing for planning and operation purpose of modern power system network. Secure and reliable operation of the power system has always been a top priority for system operators. Power system is said to be secure when it is able to withstand sudden disturbances with minimum loss of the quality of service i.e. whenever disturbance occurs, the system survives the ensuing transient and moves into a suitable stable condition where all operating constraints are within limits.
Evolutionary Algorithms (EA) such as such as genetic algorithms (GA) (Lai, & Ma, 1997), evolutionary programming (Yuryevich, & Wong, 1999; Wang, Zheng, & Tang, 2002), simulated annealing (Roa-Sepulveda & Pavez- Lazo, 2003), tabu search (Abido, 2002a; Lin, Cheng, & Tsay, 2002) and particle swarm optimization (Abido, 2002b), a hybrid tabu search and simulated annealing (TS/TA) (Ongsakul & Bhasaputra, 2002) have become the method of choice for optimization problems that are too complex to be solved using deterministic techniques such as linear programming (LP), non- linear programming (NLP), quadratic programming (QP), Newton method and interior point methods (IPM) (Momoh, El-Hawary, & Adapa, 1999; Habiabollahzadeh and Luo, 1989; Burchet, Happ, & Vierath, 1984; Mota- Palomin, & Quintana, 1986; Sun, Ashley, Brewer, Hughes, & Tinney, 1984; Yan & Quintana, 1999).
Among various OPF problems, the OPF with transient stability constraints research is an interesting problem, since it not only considers some optimal strategies but also includes all security and stability constraints. Transient stability constraints describe about the limits that system dynamics must satisfy during transient conditions to be stable. The cost of losing synchronous through a transient instability is extremely high in modern power systems.