Optimal power flow using particle swarm optimization

For pt.II see ibid., vol.14, no.1, p.96-104 (1999). This second of a two part paper offers a survey of literature on optimal power flow from 1968-93. This part treats Newton-based, linear programming and interior point methods of solution.

A review of selected optimal power flow literature to 1993. II. Newton, linear programming and interior point methods

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

Over the past half-century, Optimal Power Flow (OPF) has become one of the most important and widely studied nonlinear optimization problems. In general, OPF seeks to optimize the operation of electric power generation, transmission, and distribution networks subject to system constraints and control limits. Within this framework, however, there is an extremely wide variety of OPF formulations and solution methods. Moreover, the nature of OPF continues to evolve due to modern electricity markets and renewable resource integration. In this two-part survey, we survey both the classical and recent OPF literature in order to provide a sound context for the state of the art in OPF formulation and solution methods. The survey contributes a comprehensive discussion of specific optimization techniques that have been applied to OPF, with an emphasis on the advantages, disadvantages, and computational characteristics of each. Part I of the survey (this article) provides an introduction and surveys the deterministic optimization methods that have been applied to OPF. Part II of the survey examines the recent trend towards stochastic, or non-deterministic, search techniques and hybrid methods for OPF.

Optimal power flow: a bibliographic survey I

This paper presents an efficient and reliable tabu search (TS)-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs TS algorithm for optimal settings of the control variables of the OPF problem. Incorporation of TS as a derivative-free optimization technique in solving OPF problem significantly reduces the computational burden. One of the main advantages of TS algorithm is its robustness to its own parameter settings as well as the initial solution. In addition, TS is characterized by its ability to avoid entrapment in local optimal solution and prevent cycling by using flexible memory of search history. The proposed approach has been examined on the standard IEEE 30-bus test system with different objectives and generator cost curves. The results are promising and show the effectiveness and robustness of the proposed approach.

Optimal Power Flow Using Tabu Search Algorithm

This work presents a linear programming based algorithm to solve reactive power dispatch problems. A mixed set of variables (generated voltages and reactive power injections) and the reactive power model of the fast decoupled load flow algorithm are used to derive linear sensitivities. A suitable criterion is suggested to form a sparse reactive power sensitivity matrix. The sparse sensitivity matrix is in turn modeled as a bipartite graph which is used to define an efficient constraint relaxation strategy to solve linearized reactive power dispatch problems. The penalty function - linear programming technique method is used in a complete reactive power dispatch solution algorithm, the performance of which is discussed by solving a 256-node, 58 control-variable test system.

R. Mota-Palomino

Papers

Sparse Reactive Power Scheduling by a Penalty Function - Linear Programming Technique