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International audience; Model order reduction (MOR) has demonstrated its robustness and wide applicability in simulating large-scale mathematical models in the engineering research domain. In this paper, MOR techniques are applied to quantify relevant reliability metrics of power distribution systems and the impact associated with the integration of different smart grid technologies. To the best of the authors' knowledge, this is the first application of MOR techniques of balanced truncation to derive reliability models of electricity networks, which exhibit a reduced number of equivalent components and thus simplify the complexity for network analysis. The extensive case studies presented, based on both radial and meshed systems, demonstrate that the proposed technique allows for a faster reliability assessment through Monte Carlo simulation while preserving high accuracy. The proposed methodology can also be applied to systems endowed with photovoltaic and energy storage technologies, emphasising that this approach represents a promising starting point for reliability analysis of more complex systems, which are normally characterised by a large penetration of these distributed energy resources.

This paper analyses the effect of new smart grid technologies (SGTs) on the reliability indices typically specified by distribution network operators in low-voltage rural distribution systems. Rural areas generally denoted as “thinly-populated”, are to a large extent neglected in the anticipated transformation of existing networks into the future smart grid. An innovative Monte Carlo simulation technique is refined in this analysis to model the stochastic failure rates of power components over a specific time period, which are then applied to network load flow analysis to assess the quality of supply enhancement of a modelled rural distribution network. The proposed method enables much faster and more refined reliability studies, allowing for larger data sets to capture the inherent uncertainty from the new SGTs. Simulation results providing base case reliability indices, and the addition of SGTs accumulated from models in previous works, provide scenarios used for comparison into SGT-effectiveness.

This paper introduces the critical need to report reliability performance metrics by distinguishing between different customer-groups, load demand and network types, within very large service areas managed by distribution network operators. Based on various factors, power distribution systems supplying residential demand are categorised in this study into rural, suburban and urbannetworks. An enhanced time-sequential Monte Carlo simulation procedure is used to carry out reliability assessment for each subsector, enabling disaggregation of reliability indices typically reported for the whole supplied system. Realistic distribution network modelling is achieved by the addition of smart grid technologies such as photovoltaic energy, demand side responseand energy storage, to assess their impacts in different networks. Finally, both system and customer-oriented indices, measuring the frequency and duration of interruptions, as well as energy not supplied, are evaluated for a comprehensive analysis.

International audience; 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.

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.

Microgrid, as an emerging small-scale power system comprising a range of power sources, power electronic interfaces, loads, storage units, and being able to supply remote areas or local communities, either can be operated in islanded or grid-connected mode. Based on this concept, this paper proposes the scalability assessment and day-ahead optimization, with time-varying load and time-of-use tariff data in 48 time-periods, for multiple microgrids applied in the accommodation area in a UK university, based on an existing microgrid test system currently under investigation in its Smart Grid Laboratory. Four different scenarios, including weekdays and weekends over two seasons (summer and winter), are analyzed to achieve the optimal scheduling of the microgrid technologies. In addition, a long-term planning assessment, on optimization over 20 years, is presented to discuss the influence of microgrids' power component depreciation and life span on total energy costs and savings.

This paper applies optimal power flow (OPF) to evaluate and maximize network benefits of demand-side management (DSM). The benefits are quantified in terms of the ability of demand-responsive loads to relieve upstream network constraints and provide ancillary services, such as operating reserve. The study incorporates detailed information on the load structure and composition, and allows the potential network benefits, which could be obtained through management of different load types, to be quantified and compared. It is demonstrated that the actual network location of demand-manageable load has an important influence on the effectiveness of the applied DSM scheme, since the characteristics of the loads and their interconnecting networks vary from one location to another. Consequently, some network locations are more favorable for implementation of DSM, and OPF can be applied to determine the optimal allocation of demand-side resources. The effectiveness of the presented approach is assessed using a time-sequential OPF applied to typical radial and meshed U.K. distribution networks. The results of the analysis suggest that network operators could not just participate in, but also encourage and add value to the implementation of specific DSM schemes at the optimum network locations in order to maximize the total benefit from DSM.

This is the second paper in a two-part series discussing how Regulator requirements for continuity of supply could be incorporated in the reliability analysis of existing electricity networks and future “smart grids”. Part 1 paper presents input data, parameters and models required for a comprehensive assessment of system reliability performance, including an overview of the overall and guaranteed standards of performance in the UK and Italy. This paper presents scenarios and results of both analytical and probabilistic reliability assessment procedures for the test network introduced in Part 1 paper.

This paper is part one of a two-part series discussing how Regulator requirements for continuity of supply could be incorporated in the reliability analysis of existing electricity networks and future “smart grids”. The paper uses examples of overall and guaranteed standards of performance from the UK and Italy, specifying requirements that network operators should satisfy with respect to excessively long and/or too frequent supply interruptions. Besides the relevant Regulator requirements, this paper presents input data, parameters and models required for comprehensive reliability assessment, while Part 2 paper presents scenarios and results for test network based on both analytical and probabilistic reliability procedures.

The future “smart grids” will feature significantly higher numbers of various distributed generation and storage (DG&S) systems, as well as a wide-scale implementation of different demand-side management (DSM) functionalities. In existing literature, related analysis mostly concentrates on the operation, control and planning aspects of “smart grids”, while their impact on, and potential for improving system reliability performance is less researched. The work presented in this paper offers a new methodology for assessing reliability performance of power systems with fully employed DSM and DG&S functionalities, allowing to quantify in the most realistic manner standard set of indices reported annually to the Regulators. The methodology extends conventional reliability assessment procedures to include equivalent network models, actual load profiles and empirical fault probability distributions in the analysis, allowing to make a clear distinction between the short and long supply interruptions. Different DSM and DG&S scenarios are considered, in order to validate the proposed methodology and demonstrate its accuracy and applicability.

This paper discusses possible benefits of coordinated deployment of renewable-based micro-generation (MG) systems and energy storage schemes on reliability performance of distribution networks. Particular attention is given to continuity of supply of low voltage residential customers and potential scenarios to improve service quality by reducing the frequency and duration of customer long interruptions. Detailed network representations and improved models of aggregate loads and distributed generation/storage systems are used to assess the variable behaviour of domestic power demands and renewable energy resources. The conventional Monte Carlo Simulation (MCS) analysis is extended with an empirical probabilistic model of network faults, in order to estimate both system and customer-related reliability indices in the most realistic manner.

The paper discusses possible impact of demand-side management (DSM) functionalities on the improvement of reliability performance and formulation of novel reliability assessment procedures of future electricity networks (so called “smart grids”). Firstly, traditionally used continuity of supply metrics and indices are assessed for a given reliability test system without considering any DSM scheme. Differences in the results for reliability indices for test system modelled with bulk loads and system modelled with detailed network configurations supplying connected loads are quantified, emphasising the errors that occur when parts of the system are neglected during the estimations of reliability performance. Afterwards, the effect of DSM on the reliability performance of the same test network are analysed, in order to assess potential benefits of DSM to the network operators, particularly with respect to their annual performance reports. Finally, possible changes in used reliability metrics are discussed, as smart grids will allow to substitute standard lumped representation of system loads at higher voltage levels with a more accurate and detailed information on load demands and load structures, including estimated contribution of demand-manageable portion of system load to the total demand.

This paper, which is part one of a two-part series, analyses the influence of demand-side management (DSM) on the overall performance of distribution networks, particularly with respect to the quality of supply of low-voltage residential customers. The paper uses detailed network configurations and load models to assess changes in active/reactive power flows, system losses, voltage profiles and propagation of harmonics due to changes in the load mix as a result of DSM actions. The Part 2 paper will consider the effect of microgeneration technologies on further improvement/ deterioration of network performance.

This paper, which is the second part of a two-part series, considers the influence of microgeneration technologies on the overall network performance and quality of supply of low-voltage residential customers in future “smart grids”. The paper uses the network models and demand-side management (DSM) scenarios developed in the Part 1 paper to further assess changes in active/reactive power flows, system losses, voltage profiles and harmonic emissions due to the combined effects of implementing microgeneration, energy storage and DSM.