In this paper, we formulate the problem of finding an optimal generation dispatch and transmission topology to meet a specific inflexible load as a mixed integer program. Our model is a mixed-integer linear program because it employs binary variables to represent the state of the equipment and linear relationships to describe the physical system. We find that on the standard 118-bus IEEE test case a savings of 25% in system dispatch cost can be achieved.

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Optimal Transmission Switching

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

In this paper, we analyze the N-1 reliable dc optimal dispatch with transmission switching The model is a mixed integer program (MIP) with binary variables representing the state of the transmission element (line or transformer) and the model can be used for planning and/or operations We then attempt to find solutions to this problem using the IEEE 118-bus and the RTS 96 system test cases The IEEE 118-bus test case is analyzed at varying load levels Using simple heuristics, we demonstrate that these networks can be operated to satisfy N-1 standards while cutting costs by incorporating transmission switching into the dispatch In some cases, the percent savings from transmission switching was higher with an N-1 DCOPF formulation than with a DCOPF formulation

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Optimal Transmission Switching With Contingency Analysis

Currently, there is a national push for a smarter electric grid, one that is more controllable and flexible. The full control of transmission assets are not currently built into electric network optimization models. Optimal transmission switching is a straightforward way to leverage grid controllability: to make better use of the existing system and meet growing demand with existing infrastructure. Previous papers have shown that optimizing the network topology improves the dispatch of electrical networks. Such optimal topology dispatch can be categorized as a smart grid application where there is a co-optimization of both generators and transmission topology. In this paper we present a co-optimization formulation of the generation unit commitment and transmission switching problem while ensuring N-1 reliability. We show that the optimal topology of the network can vary from hour to hour. We also show that optimizing the topology can change the optimal unit commitment schedule. This problem is large and computationally complex even for medium sized systems. We present decomposition and computational approaches to solving this problem. Results are presented for the IEEE RTS 96 test case.

/pdf/co-optimization-of-generation-unit-commitment-and-32wcucpoye.pdf

Co-Optimization of Generation Unit Commitment and Transmission Switching With N-1 Reliability

Power system congestion is a major problem that the system operator (SO) would face in the post-deregulated era. Therefore, investigation of techniques for congestion-free wheeling of power is of paramount interest. One of the most practiced and an obvious technique of congestion management is rescheduling the power outputs of generators in the system. However, all generators in the system need not take part in congestion management. Development of sound formulation and appropriate solution technique for this problem is aimed in this paper. Contributions made in the present paper are twofold. Firstly a technique for optimum selection of participating generators has been introduced using generator sensitivities to the power flow on congested lines. Secondly this paper proposes an algorithm based on particle swarm optimization (PSO) which minimizes the deviations of rescheduled values of generator power outputs from scheduled levels. The PSO algorithm, reported in this paper, handles the binding constraints by a technique different from the traditional penalty function method. The effectiveness of the proposed methodology has been analyzed on IEEE 30-bus and 118-bus systems and the 39 -bus New England system.

Optimal Rescheduling of Generators for Congestion Management Based on Particle Swarm Optimization

Optimal network reconfiguration for congestion management by deterministic and genetic algorithms

Parallel (or loop) flows consist in the undesired circulation of power flows through certain interconnection corridors. Remedial actions available to transmission system operators or system planners include installation and operation of phase-shifting transformers and of dc transmission systems. Moreover, the invaluable experience of transmission system operators has shown that the network can be operated so as to reduce parallel flows also by properly selecting the topology of the system. In the present paper, a genetic algorithm-based procedure is designed for the topological optimization of the network against parallel flows. The control variables considered are the status of substation breakers and the location (and angle) of phase-shifting transformers. The problem is formulated as a multiobjective optimization. The main objective is that of reducing the power transfer distribution factor of an assigned transaction with reference to a set of lines; N and N-1 security levels are accounted for by means of subsidiary objective functions. The procedure is tested on a small CIGRE sample system and on a 4500-bus network representative of the European electric system (UCTE).

R. Vailati

Papers

Optimal network reconfiguration for congestion management by deterministic and genetic algorithms

A genetic algorithm-based procedure to optimize system topology against parallel flows