Reliability Enhancement in Power Networks under Uncertainty from Distributed Energy Resources
This paper presents an integrated approach for assessing the impact that distributed energy resources (DERs), including intermittent photovoltaic (PV) generation, might have on the reliability performance of power networks. A test distribution system, based on a typical urban MV and LV networks in t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/12/3/531 |
| _version_ | 1798043403374559232 |
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| author | Mike Brian Ndawula Sasa Z. Djokic Ignacio Hernando-Gil |
| author_facet | Mike Brian Ndawula Sasa Z. Djokic Ignacio Hernando-Gil |
| author_sort | Mike Brian Ndawula |
| collection | DOAJ |
| description | This paper presents an integrated approach for assessing the impact that distributed energy resources (DERs), including intermittent photovoltaic (PV) generation, might have on the reliability performance of power networks. A test distribution system, based on a typical urban MV and LV networks in the UK, is modelled and used to investigate potential benefits of the local renewable generation, demand-manageable loads and coordinated energy storage. The conventional Monte Carlo method is modified to include time-variation of electricity demand profiles and failure rates of network components. Additionally, a theoretical interruption model is employed to assess more accurately the moment in time when interruptions to electricity customers are likely to occur. Accordingly, the impact of the spatio-temporal variation of DERs on reliability performance is quantified in terms of the effect of network outages. The potential benefits from smart grid functionalities are assessed through both system- and customer-oriented reliability indices, with special attention to energy not supplied to customers, as well as frequency and duration of supply interruptions. The paper also discusses deployment of an intelligent energy management system to control local energy generation-storage-demand resources that can resolve uncertainties in renewable-based generation and ensure highly reliable and continuous supply to all connected customers. |
| first_indexed | 2024-04-11T22:48:36Z |
| format | Article |
| id | doaj.art-02b2e969a933400d8010beaa117bb496 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:48:36Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-02b2e969a933400d8010beaa117bb4962022-12-22T03:58:38ZengMDPI AGEnergies1996-10732019-02-0112353110.3390/en12030531en12030531Reliability Enhancement in Power Networks under Uncertainty from Distributed Energy ResourcesMike Brian Ndawula0Sasa Z. Djokic1Ignacio Hernando-Gil2Centre for Sustainable Power Distribution, Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UKInstitute for Energy Systems, University of Edinburgh, King’s Buildings, Mayfield Road, Edinburgh EH9 3JL, UKCentre for Sustainable Power Distribution, Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UKThis paper presents an integrated approach for assessing the impact that distributed energy resources (DERs), including intermittent photovoltaic (PV) generation, might have on the reliability performance of power networks. A test distribution system, based on a typical urban MV and LV networks in the UK, is modelled and used to investigate potential benefits of the local renewable generation, demand-manageable loads and coordinated energy storage. The conventional Monte Carlo method is modified to include time-variation of electricity demand profiles and failure rates of network components. Additionally, a theoretical interruption model is employed to assess more accurately the moment in time when interruptions to electricity customers are likely to occur. Accordingly, the impact of the spatio-temporal variation of DERs on reliability performance is quantified in terms of the effect of network outages. The potential benefits from smart grid functionalities are assessed through both system- and customer-oriented reliability indices, with special attention to energy not supplied to customers, as well as frequency and duration of supply interruptions. The paper also discusses deployment of an intelligent energy management system to control local energy generation-storage-demand resources that can resolve uncertainties in renewable-based generation and ensure highly reliable and continuous supply to all connected customers.https://www.mdpi.com/1996-1073/12/3/531demand profilesdemand responsedistributed (PV) generationenergy storagefailure rateMonte Carlo simulationnetwork reliabilityrenewable resources |
| spellingShingle | Mike Brian Ndawula Sasa Z. Djokic Ignacio Hernando-Gil Reliability Enhancement in Power Networks under Uncertainty from Distributed Energy Resources Energies demand profiles demand response distributed (PV) generation energy storage failure rate Monte Carlo simulation network reliability renewable resources |
| title | Reliability Enhancement in Power Networks under Uncertainty from Distributed Energy Resources |
| title_full | Reliability Enhancement in Power Networks under Uncertainty from Distributed Energy Resources |
| title_fullStr | Reliability Enhancement in Power Networks under Uncertainty from Distributed Energy Resources |
| title_full_unstemmed | Reliability Enhancement in Power Networks under Uncertainty from Distributed Energy Resources |
| title_short | Reliability Enhancement in Power Networks under Uncertainty from Distributed Energy Resources |
| title_sort | reliability enhancement in power networks under uncertainty from distributed energy resources |
| topic | demand profiles demand response distributed (PV) generation energy storage failure rate Monte Carlo simulation network reliability renewable resources |
| url | https://www.mdpi.com/1996-1073/12/3/531 |
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