Adaptive and Dynamic Ant Colony Search Algorithm for Optimal Distribution Systems Reinforcement Strategy |
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Authors: | S Favuzza G Graditi E Riva Sanseverino |
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Affiliation: | (1) Dipartimento di Ingegneria Elettrica, Elettronica e delle Telecomunicazioni, Università di Palermo, Italia;(2) Centro ricerche ENEA, Ente per le Nuove Tecnologie, l'Energia e l'Ambiente, Portici, Napoli, Italia;(3) Dipartimento di Ingegneria Elettrica, Elettronica e delle Telecomunicazioni, Università di Palermo, Italia |
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Abstract: | The metaheuristic technique of Ant Colony Search has been revised here in order to deal with dynamic search optimization problems
having a large search space and mixed integer variables. The problem to which it has been applied is an electrical distribution
systems management problem. This kind of issues is indeed getting increasingly complicated due to the introduction of new
energy trading strategies, new environmental constraints and new technologies. In particular, in this paper, the problem of
finding the optimal reinforcement strategy to provide reliable and economic service to customers in a given time frame is
investigated. Utilities indeed need efficient software tools to take decisions in this new complex scenario. In past times,
utilities project the load growth for several years and then estimate when the capacity limit will be exceeded. Designers
then consider some feasible alternatives and select the optimal one in terms of performance and costs. In this paper, the
Distributed Generation, DG, technology considered in compound solutions with the installation of feeder and substations is
viewed as a new option for solving distribution systems capacity problems, along several years. The objective to be minimized
is therefore the overall cost of distribution systems reinforcement strategy in a given timeframe. An application on a medium
size network is carried out using the proposed technique that allows the identification of optimal paths in extremely large
or non-finite spaces. The proposed algorithm uses an adaptive parameter in order to push exploration or exploitation as the
search procedure stops in a local minimum. The algorithm allows the easy investigation of these kinds of complex problems,
and allows to make useful comparisons as the intervention strategy and type of DG sources vary. |
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Keywords: | ant colony search dynamic optimization problems electrical distribution systems |
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