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.

Electricity-distribution network operators face several operational constraints in the provision of safe and reliable power given that investments for network area reinforcement must be commensurate with improvements in network reliability. This paper provides an integrated approach for assessing the impact of different operational constraints on distribution-network reliability by incorporating component lifetime models, time-varying component failure rates, as well as the monetary cost of customer interruptions in an all-inclusive probabilistic methodology that applies a time-sequential Monte Carlo simulation. A test distribution network based on the Roy Billinton test system was modelled to investigate the system performance when overloading limits are exceeded as well as when preventive maintenance is performed. Standard reliability indices measuring the frequency and duration of interruptions and the energy not supplied were complemented with a novel monetary reliability index. The comprehensive assessment includes not only average indices but also their probability distributions to adequately describe the risk of customer interruptions. Results demonstrate the effectiveness of this holistic approach, as the impacts of operational decisions are assessed from both reliability and monetary perspectives. This informs network planning decisions through optimum investments and consideration of customer outage costs.

This paper evaluates deterministic and probabilistic approaches for assessing hosting capacity (HC) of distribution networks for wind-based distributed generation (DG). The presented methodology considers variations of demands and DG power outputs, as well as dynamic thermal ratings (DTR) of network components. Deterministic approaches are based on a limited number of scenarios with minimum and maximum demands and DTR limits, while probabilistic approaches use simultaneous hourly values of all input parameters. The presented methodology has three stages. First, locational HC (LHC) of individual buses is calculated assuming connection of a single DG unit in the considered network. Afterwards, the LHC results are used to calculate network HC (NHC), assuming that DG units are connected at all network buses. Finally, busto-bus LHC-sensitivity factors are used to determine LHC and NHC for any number of DG units connected at arbitrary network buses.

This paper presents an integrated approach for assessing the impact that distributed energy resources (DERs), mostly intermittent in nature, might have on the reliability performance of distribution networks. A test distribution system based on a typical MV/LV urban distribution network in the UK is fully modelled and controlled to investigate the potential benefits that local renewables and energy storage can offer to the quality of power supply to customers. In this analysis, the conventional Monte Carlo method is further developed to include the 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, with photovoltaic (PV) systems as key enablers, is quantified in terms of the effect of network outages. A range of smart grid functionalities is analysed and their benefits are assessed through standard reliability indices, with special attention to energy not supplied to customers, as well as frequency and duration of supply interruptions.

This paper presents a reliability-based approach for the design and deployment of an energy management system (EMS) by using `smart' applications, such as energy storage (ES), to control battery power output in residential dwellings, and thus improve distribution-network reliability performance. The state of charge (SOC) of the battery system is designed based on time-varying electricity tariff, load demand and solar photovoltaic (PV) generation data to investigate a realistic test-case scenario. Additionally, a typical MV/LV urban distribution system is fully modelled and scripted to investigate the potential benefits that `smart' interventions can offer to customers' quality of power supply. In this research, Monte-Carlo simulation method is further developed to include the time-variation of electricity demand profiles and failure rates of network components. Accordingly, the reliability-based effects from SOC variation in batteries are compared with an uncontrolled microgeneration (MG) scenario, by using different PV penetration levels to justify the value of control. The benefits are assessed through standard reliability indices measuring frequency and duration of power interruptions and most importantly, the energy not supplied to customers during sustained interruptions.

Electricity distribution network operators face several operational constraints in the provision of safe and reliable power given that investments for network area reinforcement must be commensurate with improvements in network reliability. This paper provides an integrated approach for assessing the impact of different operational constraints on distribution network reliability by incorporating component lifetime models, time-varying component failure rates as well as the monetary cost of customer interruptions in an all-inclusive probabilistic methodology that applies a time-sequential Monte Carlo simulation. A test distribution network based on the Roy Billinton test system is modelled to investigate the system performance when overloading limits are exceeded as well as when preventive maintenance is performed. Standard reliability indices measuring the frequency and duration of interruptions, and the energy not supplied are complemented with a novel monetary reliability index. The comprehensive assessment includes not only average indices but also their probability distributions to adequately describe the risk of customer interruptions. Results demonstrate the effectiveness of this holistic approach as the impacts of operational decisions are assessed from both reliability and monetary perspectives. This informs network planning decisions through optimum investments and consideration of customer outage costs.