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Existing educational buildings built with old normatives suffer a lack of technology, eventhough they are the frame of reference for our future society’s architects(/builders). These buildings, usually promoted by the public sector, don’t have significant economic investment, even if they are going to affect our children’s world perception. The object construction of this study is a building from1978. It has an educational use located at the Pamplona campus of the Universidad de Navarra. The building is part of the Living Lab of the Campus, where technologies, solutions and strategies can be proved It is a protected building by the “Documentation and Conservation of buildings, sites, and neighborhoods of the Modern Movement” (Do. Co,Mo.Mo.), so all actions that can affect its aesthetic aspects need to be justified because of the value of the building.The methodology presented concerns the development of a replicable technical, constructive, and economical feasibility model to reach an off-grid disconnection of an existing building. The steps followed for this study are classified in seven main steps. This proposal aims to define a replicable solution that is going to be applied to other buildings of campus Universidad de Navarra in Pamplona.Even that the first solution is going to be limited by the aesthetic aspect, the final objective is to develop a Plug & Play solution following the methodology, answering the energetical deficiency and complexity of existing constructions.

Effective utility system management is fundamental and critical for ensuring the normal activities, operations, and services in cities and urban areas. In that regard, the advanced information and communication technologies underpinning smart cities enable close linkages and coordination of different subutility systems, which is now attracting research attention. To increase operational efficiency, we propose a two-stage optimal co-management model for an integrated urban utility system comprised of water, power, gas, and heating systems, namely, integrated water-energy hubs (IWEHs). The proposed IWEH facilitates coordination between multienergy and water sectors via close energy conversion and can enhance the operational efficiency of an integrated urban utility system. In particular, we incorporate social-aware peer-to-peer (P2P) resource trading in the optimization model, in which operators of an IWEH can trade energy and water with other interconnected IWEHs. To cope with renewable generation and load uncertainties and mitigate their negative impacts, a two-stage distributionally robust optimization (DRO) is developed to capture the uncertainties, using a semidefinite programming reformulation. To demonstrate our model’s effectiveness and practical values, we design representative case studies that simulate four interconnected IWEH communities. The results show that DRO is more effective than robust optimization (RO) and stochastic optimization (SO) for avoiding excessive conservativeness and rendering practical utilities, without requiring enormous data samples. This work reveals a desirable methodological approach to optimize the water–energy–social nexus for increased economic and system-usage efficiency for the entire (integrated) urban utility system. Furthermore, the proposed model incorporates social participations by citizens to engage in urban utility management for increased operation efficiency of cities and urban areas.

International audience; 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.

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.

International audience; Due to decarbonisation and decentralisation of energy sectors, the rise of distributed generation (DG) will modify generation and demand patterns at grid supply points (GSPs), where the interface between distribution and transmission systems takes place. With the increasing penetration of such devices, methodologies able to evaluate its contribution to both local distribution network and transmission system become crucial. This paper proposes an analytical method to evaluate the DG locational incremental contribution (LIC) to the interface with the transmission grid. Accordingly, the original model of the UK engineering recommendation P2 is enhanced by studying the impacts from high-voltage distribution networks (i.e. ≤ 132 kV) under normal and contingent conditions. This approach enables a more accurate network security assessment, especially when considering contingencies such as distribution system faults. To illustrate the proposed method, the original P2 model is compared against different enhanced approaches on three basic distribution networks, followed by a case study and a sensitivity analysis on a revised IEEE 14-bus GSP system. The proposed LIC method produces results that assess a wider range of conditions including DG penetration, concentration, and system reliability. Furthermore, it provides an increased DG visibility for transmission planning and operation.

This paper develops a novel two-stage coordinated volt-pressure optimization (VPO) for integrated energy systems (IES) networked with energy hubs considering renewable energy sources. The promising power-to-gas (P2G) facilities are used for improving the interdependency of the IES. The proposed VPO contains the traditional volt-VAR optimization functionality to mitigate the voltage deviation while ensuring a satisfying gas quality due to the hydrogen mixture. In addition to the conventional voltage regulating devices, i.e., on-load tap changers and capacitor banks, P2G converter and gas storage are used to address the voltage fluctuation problem caused by renewable penetration. Moreover, an effective two-stage distributionally robust optimization (DRO) based on Wasserstein metric is utilized to capture the renewable uncertainty with tractable robust counterpart reformulations. The Wasserstein-metric based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Extensive case studies are conducted in a modified IEEE 33-bus system connected with a 20-node gas system. The proposed VPO provides a voltage-regulated economic operation scheme with gas quality ensured that contributes to high-quality but low-cost multi-energy supply to customers. British Council (Grant Number: 515761951); National Natural Science Foundation of China (Grant Number: 72042018 and 71621002).

International audience; 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.

Power-to-gas (P2G) can convert excessive renewable energy into hydrogen via electrolysis, which can then be transported by natural gas systems to bypass constrained electricity systems. However, the injection of hydrogen could impact gas security since gas composition fundamentally changes, adversely effecting the combustion, safety and lifespan of appliances. This paper develops a new gas security management scheme for hydrogen injection into natural gas systems produced from excessive wind power. It introduces four gas security indices for the integrated electricity and gas system (IEGS) measuring gas security, considering the coordinated operation of tightly coupled infrastructures. To maintain gas security under an acceptable range, the gas mixture of nitrogen and liquid petroleum gas with hydrogen is adopted to address the gas security violation caused by hydrogen injection. A distributionally robust optimization (DRO) modelled by Kullback-Leibler (KL) divergence-based ambiguity set is applied to flexibly control the robustness to capture wind uncertainty. The KL divergence-based ambiguity set defines uncertainties within a measured space which limits the shape of probability distributions. Case studies illustrate that wind power is maximally utilized and gas mixture is effectively managed, thus improving gas security and performance of IEGS. This work can bring many benefits: i) ensured gas security under hydrogen injection ii) low system operation cost and iii) high renewable energy penetration. It can be easily extended to manage injections of other green gases into IEGS.

High temperature Superconducting Magnetic Energy Storage (SMES) systems can exchange energy with substantial renewable power grids in a small period of time with very high efficiency. Because of this distinctive feature, they store the abundant wind power when the power network is congested and release the energy back to the system when there is no congestion. However, considering the cost and lifespan of SMES systems, there is an urgent demand to conduct a cost-benefit analysis to justify its role in smart grid development. This study explores the application and performs economic analysis of a 5 MJ SMES in a practical renewable power system in China based on the PSCAD/ EMTDC software. An optimal location of SMES in Zhangbei wind farm is presented using real power transmission parameters. The stabilities of the renewable power grid with and without SMES are discussed. In addition, a financial feasibility study is conducted by comparing the cost and the savings from wind power curtailment of deploying SMES and battery. The economic analysis tries to find the balance between SMES investment cost and wind farm operation cost by using real data over a calendar year. The technical analysis can help guide the optimal allocation of SMES for compensating power system instability with substantial wind power. Further, the economic analysis provides a useful indication of its practical application feasibility to fight the balance between cost and benefit. Keywords: High temperature Superconducting Magnetic Energy Storage (SMES) system, Renewable energy, Smart grid, Allocation planning, Economic planning, Large-scale energy storage

In this paper, typical strengths, fault levels, and source impedances are thoroughly analyzed and calculated for the study of quality of supply in 230/400 V 50 Hz distribution systems. Considering all the disparity in distribution network design, this study is based on a comprehensive database containing typical arrangements and equipment in U.K./European systems, as well as on fully documented generic network models supplying four residential load subsectors in the U.K., i.e., from metropolitan to rural areas. Thus, this paper proposes an alternative method for determining reference values of network supply impedances and short-circuit fault levels at different points and locations of the medium-to-low voltage distribution system. The aim of this study is to provide a wider range of benchmark values than those stipulated in the IEC 60725 Standard, which only defines a single-reference threshold of public supply impedances for all types of distribution systems and residential customers. In order to assist network operators in the planning and design of their distribution systems, these values are further disaggregated and classified in this paper according to network/demand type.

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.

Energy storage and demand response (DR) resources, in combination with intermittent renewable generation, are expected to provide domestic customers with the ability to reducing their electricity consumption. This study highlights the role that an intelligent battery control, in combination with solar generation, could play to increase renewable uptake while reducing customers' electricity bills without intruding on people's daily life. The optimal performance of a home energy management system (HEMS) is investigated through a range of interventions, leading to different levels of customer weariness and consumption patterns. Thus, the DR is applied with efficient and specific control of domestic appliances through load shifting and curtailment. Regarding the uncertainty associated with the photovoltaic generation, a chance-constrained (CC) optimal scheduling is considered subject to the operation constraints from each power component in the HEMS. By applying distributionally robust optimisation, the ambiguity set is accurately built for this distributionally robust CC (DRCC) problem without the need for any probability distribution associated with uncertainty. Based on the greatly altered consumption profiles in this study, the proposed DRCC-HEMS is proven to be optimally effective and computationally efficient while considering uncertainty.

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 study presents an integrated approach for reliability planning and risk estimation in active distribution systems. By incorporating the use of accurate reliability equivalents for different medium voltage/low voltage networks and load subsectors, a probabilistic methodology is proposed to capture both power quality and reliability aspects in power system planning, which potentially avoids the underestimation of system's performance at bulk supply points. A ‘time to restore supply’ concept, based on security of supply legislation, is introduced to quantify the effect of different network functionalities such as the use of backup supply or automatic/manual reconfiguration schemes. The range of annual reliability indices reported by 14 network operators in the UK is also used for the validation of reliability results, which allows estimating the risk of interruption times above the regulator-imposed limits. Accordingly, conventional reliability assessment procedures are extended in this study by analysing a meshed urban distribution network through the application of a time-sequential Monte Carlo simulation. The proposed methodology also acknowledges the use of time-varying fault probabilities and empirical load profiles for a more realistic estimation of customer interruptions. A decision-making approach is shown by assessing the impact of several network actions on the accuracy of reliability performance results.

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 study introduces a new theoretical interruption model for assessing more accurately the moment in time when interruptions of electricity customers are likely to occur. Recordings of short and long interruptions from two power supply systems are analysed and the similarity between their patterns is identified and then used to introduce a general interruption probability distribution model, defined in stages as multi-zone theoretical curves. The effectiveness of the proposed theoretical interruption model is firstly verified for a basic test system supplying an aggregate load point whose power profiles (residential, commercial, industrial and mixed load) are engaged in assessing the energy not supplied, and afterwards for a typical UK power supply system consisting of about 15 000 electricity customers. The results show that a correct representation of the moment of interruption performed with the proposed model leads to completely different results than those obtained based on the conventional assumption that the time when interruption occurs is given by a known probability distribution. Moreover, comparisons against reported figures of reliability indices determine the most suitable probability distribution that shall be used to model the initial conditions of the Monte Carlo simulation and accompany the proposed theoretical model throughout the simulation process.

Legislation defined to protect domestic and non-domestic customers from long durations of interruptions includes additional requirements to system’s reliability-related performance that distribution network operators (DNOs) must consider in planning the operation and maintenance process of power supply systems. DNOs are required to restore the supply to interrupted customers that fall into “unprotected” customer class within a given period of time, otherwise penalties are applied. In order to meet these requirements, comprehensive strategies must be defined based on upfront analyses. Accordingly, this paper proposes a deterministic algorithm for estimating DNOs’ risk of experiencing interruptions with durations above imposed targets. Besides the Regulator-defined legislation, security of supply requirements are engaged in the development of the proposed methodology. Failure analysis of network components is used to identify interrupted customers that are grouped into power demand classes such that the duration of interruptions can be addressed following the security of supply requirements. Moreover, the penalty times defined by the Energy Regulator are engaged in the analysis and used as thresholds to quantify the penalty risk that DNOs are exposed to. The proposed methodology is applied to a typical UK distribution system, whose average reliability performance is also considered in the analysis.

This paper, which can be divided into two main interrelated studies, firstly describes the generic modelling of a sub-transmission network model to serve as a UK variant of the original IEEE 14-bus test system. The revised model, based on the actual/realistic power components found both in the UK and in European grids, provides an updated and complete technical description, ready for use in a variety of power system studies, in which the 14-bus test system is one of the most commonly used in the literature. Afterwards, this paper categorises the typical demand characteristics of the residential load sector in the UK, by providing a wider range of reference demand values and loading conditions for the planning and modelling studies of distribution networks, dividing them into four generic residential load subsectors. Different ‘after diversity demand’ values are therefore provided per residential load subsector, classes of customers and seasonal variations of annual power consumption.

This paper addresses some general issues related to the difficulties and uncertainties during the assessment and characterization of micro and small-scale wind-based generation in urban areas. This paper proposes four generic wind turbine models, which could be used for the analysis and selection of optimal wind turbines in target applications, as they accurately represent the range of wind turbines currently available on the market. The analysis in this paper compares results for the expected annual energy outputs and cost-benefit analysis obtained using steady state and dynamic wind turbine models of actual and generic micro/small wind turbines, applying both low-resolution and high-resolution measurements of available wind energy resources. The presented results demonstrate the significance of the accurate assessment of wind resources in urban areas, as well as the importance of the correct modeling of wind turbine characteristics. It is shown that substantial errors, typically overestimating wind turbine performance, are obtained if standard assumptions and current recommendations are used for the analysis of wind turbine performance.

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.

This paper, which is the second part of a two-part series, considers the influence of distributed energy resource functionalities on reliability performance of active networks. The reliability and network equivalent models defined in the Part 1 paper are used to assess the potential improvements that different demand-side management and energy storage schemes will have on the frequency and duration of customer interruptions. Particular attention is given to energy-related reliability indices which measure the energy and power not supplied to residential and commercial customers. A new theoretical interruption model is also introduced for a more accurate correlation between the different low-voltage and medium-voltage demand profiles and the time when both long and short interruptions are more likely to occur.

This paper, which is part one of a two-part series, presents a general methodology for reducing system complexity by calculating the electrical and reliability equivalent models of low and medium voltage distribution networks. These equivalent models help to reduce calculation times while preserving the accuracy assessment of power system reliability performance. The analysis is applied to typical UK distribution systems, which supply four generic load sectors with different networks and demand compositions (residential, commercial and industrial). This approach allows for a direct correlation between reliability performance and network characteristics, while assessing the most representative aggregate values of failure rates and repair times of power components at each load sector. These are used in the Part 2 paper for assessing the potential benefits of energy storage and demand-side resources on the reliability performance of different generic distribution networks.

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.

This paper presents a simple methodology for the formulation of accurate reliability equivalent models of LV and MV parts of larger power supply systems. Calculated failure rates and mean repair times of the equivalent part of the network are assigned to the corresponding bulk load supply point so that the original power supply system can be correctly represented by a reduced model during the conventional reliability analysis. The accuracy and effectiveness of the equivalenting methodology are demonstrated through a number of different scenarios applied to a test system, representing the typical UK distribution network configurations.

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 is part two of a two-part series on the development of improved aggregate load models for steady state power system analysis. Part one paper presented a general methodology for building component-based low voltage (LV) aggregate load models from statistical information on load structure and measurements of active/reactive power demands. The developed LV aggregate load models are then connected to typical LV and medium voltage (MV) network configurations, in order to obtain correct aggregate load models at higher voltage levels. This paper discusses how the presented LV and MV aggregate load models can be modified in order to include microgeneration technologies and demand-side management (DSM) functionalities in the analysis. Using the UK residential load sector as an example, it is shown that the assessment of microgeneration should be correlated both spatially and temporally with the aggregated load. The effects of DSM generally depend on the applied scenario, but may have a more pronounced effect on the aggregate demands.

This paper, which is part one of a two-part series, presents a general methodology for the development of improved aggregate load models for steady state power system analysis. Using the UK residential load sector as an example, the paper shows how component-based low voltage (LV) aggregate load models can be obtained using measurements, statistical and other available data and information on load structure and active/reactive power demands. The presented aggregate LV load models are connected to typical LV and medium voltage (MV) network configurations, in order to obtain correct aggregate load models at higher voltage levels. Part two paper discusses how the presented aggregate load models can be modified to include the analysis the effects of demand side management and microgeneration technologies have on the overall performance of LV/MV networks.