This paper presents an enhanced genetic algorithm (EGA) for the solution of the optimal power flow (OPF) with both continuous and discrete control variables. The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. A number of functional operating constraints, such as branch flow limits, load bus voltage magnitude limits, and generator reactive capabilities, are included as penalties in the GA fitness function (FF). Advanced and problem-specific operators are introduced in order to enhance the algorithm's efficiency and accuracy. Numerical results on two test systems are presented and compared with results of other approaches.

Optimal power flow by enhanced genetic algorithm

A survey is presented of publications in the fields of optimal power flow and dispatching. It suggests a classification of methods based on the choice of optimization techniques. The survey is summarized in a single flow-chart-type figure, which indicates the relationship between methods, their chronology, and their popularity. This figure is based on a compilation of over three hundred publications. >

A survey of the optimal power flow literature

An operationally "secure" power system is one with low probability of blackout or equipment damage. The power system control processes needed to maintain a designated security level at minimum operating cost are extremely complicated. They increasingly depend upon on-line computer security analysis and optimization. This on-line technology is still relatively new, with enormous further potential. Since security and optimality are normally conflicting requirements of power system control, it is inappropriate to treat them separately. Therefore, they are slowly becoming coalesced into a unified hierarchical mathematical problem formulation: one that is, however, far too complex to afford anything but an approximate, near-optimal solution. The practical validity of this unifying trend relies on being able to incorporate all significant security constraints within the process. The main two current computational tools in this field are contingency analysis and special operations-oriented versions of optimal power flow (OPF). Contingency analysis identifies potential emergencies through extensive "what if?." simulations on the power system network. OPF is a major extension to the conventional dispatch calculation. It can respect system static security limits, and can schedule reactive as well as active power. Moreover, the advanced versions of OPF include or interface with contingency analysis. This paper reviews present formulations and methods, and tries to point out areas of difficulty that constitute the main challenges for successful practical on-line implementations over the coming years.

Security analysis and optimization

The authors describe developments that have transformed the LP (linear programming) approach into a truly general-purpose OPF (optimal power flow) solver, with computational and other advantages over even recent nonlinear programming (NLP) methods. it is pointed out that the nonseparable loss-minimization problem can now be solved, giving the same results as NLP on power systems of any size and type. Coupled formulations, where for instance voltages and VAr become constraints on MW scheduling, are handled. Former limitations on the modeling of generator cost curves have been eliminated. In addition, the approach accommodates a large variety of power system operating limits, including the very important category of contingency constraints. All of the reported enhancements are fully implemented in the production OPF software described here, and most have already been utilized within the industry. >

Further developments in LP-based optimal power flow

State-of-the-art, challenges, and future trends in security constrained optimal power flow

Optimal power flow programs have deficiencies that limit their practical value and scope of application. Three of these deficiencies are: (1) the use of equivalents causes errors, (2) the methods for adjusting discrete variables are suboptimal; and (3) the number of control actions is too large to be executed. These deficiencies are explained, and approaches for correcting them are discussed. >

W.F. Tinney

Papers

Some deficiencies in optimal power flow