Optimal Power Flow Packages Requirements and Experiences

Abstract

The purpose of an Optimal Power Flow (OPF) function is to schedule the power system controls to optimize an objective function while satisfying a set of nonlinear equality and inequality constraints. The equality constraints are the conventional power flow equations; the inequality constraints are the limits on the control and operating variables of the system. Mathematically the OPF can be formulated as a constrained nonlinear optimization problem.

Practical, constrained active and reactive OPF problems have complicated non-analytical, non-static and partially discrete formulations. At the same time, however, most OPF development efforts have centered on the mathematical optimization of simple classical OPF formulations, expressed in smooth nonlinear programming form. As they stand, these formulations are far too approximate and incomplete descriptions of the real life problems to be adequate for on-line use. Furthermore, at the present time they cover only a limited area of system operations.

This paper will address specific operational requirements that need to be met for a successful implementation and use of an on-line OPF package. These include a) response time requirements, b) robustness with respect to starting point, c) expansion of the scope of the OPF problem to be able to solve realistically posed problems, d) infeasibility detection and handling, e) ineffective “optimal” rescheduling, f) discrete modeling, g) development of techniques/guidelines for selecting an “optimal trajectory” that steers the power system as reliably and as far as possible in the direction of the optimum, h) modeling of contingency constraints, i) consistency of OPF with other on-line functions, j) data quality and other practical requirements, k) maintenance and MMI and 1) on-line OFF based external modeling.

Over the last two decades several approaches have been proposed to solve the constrained nonlinear OPF problem. A straightforward solution could be to use quadratic programming Other approaches that have been implemented with various degrees of success include, Generalized Reduced Gradient, Newton’s method, Linear Programming and Interior Point techniques. Based on the above requirements, experience and conclusions reached from the development and/or use in a practical environment of these techniques will also be discussed.