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This paper proposes a non-linear optimization Interior Point (IP) method for the determination of maximum loadability in a power system. Details of the implementation of pure primal-dual and predictor-corrector primal-dual IP algorithms are presented. It is shown that most of the computational effort of the algorithm is taken by the formation and factorization of the augmented Hessian matrix of the IP algorithm. The size of this matrix can be as large as ten times the number of buses in the system. Comparisons of the two IP implementations with large scale power systems with as many as 4000 buses are presented. It is shown that the IP algorithm constitutes an effective method for the determination of the maximum loadability in a power system. Keywords: Power system optimization, maximum loadability, interior point algorithms, voltage stability.

The comparison of performance of four methods for contingency screening and ranking for voltage stability analysis is presented. Three of the methods are existing methods, while a new method is proposed. The performance of all the methods is carried out by comparing with full solutions using a continuation power flow. It is shown that the newly proposed method has the best performance in terms of accuracy and computation time. All the methods are evaluated using a 234-bus system. Additional results using the best performing method are included for a 901-bus power system. Keywords: Voltage stability, continuation power flow, contingency screening and ranking, saddle node bifurcation, voltage collapse.

This paper discusses the Issues which must be addressed when integrating Artificial Intelligence (AI) and, in particular, expert system applications in an Energy Management System (EMS) environment. It is argued that these issues can be resolved by creating an environment which supports all the interfaces between the Artificial Intelligence (AI) applications and the EMS. This environment should also be responsible for maintaining a model of the power system common to all the AI applications. Once this environment has been created, AI applications can be easily 'plugged' into the EMS. The design of such an environment is described and case studies of its implementation are provided to illustrate its flexibility. Keywords: Power systems, artificial intelligence, energy management systems, topology processing.

This paper presents an efficient approach to short term resource scheduling based on the augmented Lagrangian relaxation method. The problem is divided into two stages, the commitment stage and the constrained economic dispatch stage. The proposed mathematical model incorporates optimal power, flow (OPF) constraints in the unit commitment stage. By OPF constrains, we refer to the relevant active power constraints that are incorporated in the constrained economic dispatch stage (i.e. transmission capacity constraints, fuel and various regulated emission requirements). The inclusion of OPF constraints in the commitment stage will improve the feasibility of the constrained economic dispatch solution. Other unit commitment constraints such as spinning and operating reserve requirements, power balance as well as other relevant local constraints (i.e. unit ramping rates, upper and lower generation limits, minimum up and down times) are taken into account in the proposed model. As we deal with a larger number of constraints, a more rigorous method is introduced for updating Lagrange multipliers to improve the solution convergence. A software package which addresses energy management systems requirements is developed and tested. Keywords: Resource Scheduling, Augmented Lagrangian Relaxation, Fuel Constraints, OPF Constraints