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A fast interactive optimal load management algorithm under time-varying tariffs

Identifieur interne : 003B80 ( Main/Merge ); précédent : 003B79; suivant : 003B81

A fast interactive optimal load management algorithm under time-varying tariffs

Auteurs : C. S. Chang [République populaire de Chine]

Source :

RBID : ISTEX:781E819E75CFEA3C3F9062BD8267D52DBB8C2423

Abstract

A new approach to electricity tariff structuring, in which the tariff is varied continuously according to the actual production cost of electricity, has attracted many research workers. This paper begins by defining an analytical framework, using which each consumer is able to maximize his returns. Under this framework, a conceptually simple and yet computationally efficient algorithm has been developed to help each consumer in achieving his own goal. Based on the method of Lagrange multipliers, each method for improving the consumption pattern is ranked according to its incremental cost of implementation. In the first numerical example the algorithm provides a medium-size industrial consumer with the choice between self-generation and load shifting. In the second numerical example well-managed loads are shown to make good returns for the supplier and the resulting load factor is improved, leading to savings in fuel and capital costs.

Url:
DOI: 10.1016/0142-0615(88)90014-2

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Links to Exploration step

ISTEX:781E819E75CFEA3C3F9062BD8267D52DBB8C2423

Le document en format XML

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<div type="abstract" xml:lang="en">A new approach to electricity tariff structuring, in which the tariff is varied continuously according to the actual production cost of electricity, has attracted many research workers. This paper begins by defining an analytical framework, using which each consumer is able to maximize his returns. Under this framework, a conceptually simple and yet computationally efficient algorithm has been developed to help each consumer in achieving his own goal. Based on the method of Lagrange multipliers, each method for improving the consumption pattern is ranked according to its incremental cost of implementation. In the first numerical example the algorithm provides a medium-size industrial consumer with the choice between self-generation and load shifting. In the second numerical example well-managed loads are shown to make good returns for the supplier and the resulting load factor is improved, leading to savings in fuel and capital costs.</div>
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