Abstract

To improve electrical distribution network reliability, some portions of the network could operate in autonomous mode, provided that the related technical issues are addressed. More specifically, when there is not a path from those portions to the primary substation due to a fault in the network, such portions could be disconnected from the main network and supplied by local generation only. Such a mode of operation is known as “intentional islanding” and its effectiveness, in terms of adequacy, depends on the ability of the local generation to meet the island’s load. In fact, the ratio between the available local generation and load demand can frequently change during islanding due to load variations and, especially, due to the strongly irregular behavior of the primary energy sources of renewable generators. This paper proposes an analytical formulation to assess local generation adequacy during intentional islanding, accounting for the aforementioned variations. More specifically, the fluctuations of load and green-energy generators during islanding are modeled by means of Markov chains, whose output quantities are encompassed in the proposed analytical formulation. Such a formulation is used by the analytical equations of load points’ outage rate and duration. The evaluation of the reliability indices accounts for a protection scheme based on an appropriate communication infrastructure. Therefore, a brief overview on the telecommunications technologies has been presented with reference to their suitability for the specific application. In particular, distribution network safety issues have been considered as the main concern. The results show that neglecting load and generation fluctuations leads to a strong overestimation of the ability of distributed generators to meet the island load. Through a case study it is observed that the error on the load point outage rate is greater than the one affecting the outage duration.

Highlights

  • The deregulation of the electricity market and the introduction by national authorities of reward/penalty mechanisms are driving distribution network operators (DNOs) to improve the reliability performance of distribution systems [1]

  • The annual load model related to a load point (LP) i is usually obtained by means of the historical power demand of the customers connected to the LP

  • The models of an equivalent load and an equivalent generator in an island are defined. These models are combined to obtain Rj,m, as calculated in Equation (3), in order to derive the Probability of Adequacy (PoA) more accurately, that is accounting for the time variations of load and generation that usually occur when the island mode is maintained for some hours

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Summary

Introduction

The deregulation of the electricity market and the introduction by national authorities of reward/penalty mechanisms are driving distribution network operators (DNOs) to improve the reliability performance of distribution systems [1]. The present work proposes an efficient computational approach that is able to overcome the limitation of the previous ones and account for load and generation correlations, obtaining a very accurate measure of the reliability improvement that can be achieved thanks to islanding in distribution networks where telecontrolled CBs as well as telecontrolled and manual sectionalizers are installed. In order to calculate distribution network reliability, the analytical equations of load point outage rate and duration that account for a protection scheme based on telecontrolled switches [4] are combined with the formulation proposed for adequacy computation Such a formulation uses a Markov chain for modeling the ratio between load and generation.

Annual Model of Loads and Distributed Generators
Comparison
Markov Models for Adequacy Evaluation
Markov Model of the Island’s Load
Markov Model of the Island’s Generation
Markov Model of the Ability of the Generation to Meet the Island Load
PoA Assessment Considering Load and Generation Fluctuations in an Island
Case Study
Overestimationofofthe theProbability
Probability
Findings
Conclusions

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