Estimating national and local low-voltage grid capacity for residential solar photovoltaic in Sweden, UK and Germany

The electric grid\u27s available capacity to accommodate solar photovoltaic on national scales is currently uncertain. This makes decisions about grid capacity expansion, which can be very costly for local grid operators, difficult to make. Yet, knowledge of national solar photovoltaic grid capacity is central in order to formulate realistic solar PV targets and strategies. We present a methodology based on publicly available data to estimate the grid\u27s hosting capacity of residential solar photovoltaic at both the national and local scale. The model is applied to Sweden, Germany and the UK and shows that low-voltage grid capacity for residential solar photovoltaic is very large, 33 (+5/-7) GW (Sweden), 248 (+5/-24) GW (Germany) and 63 (+1/-14) GW UK, and similar to current total generation capacity. Based on our estimations, we find that with the capacity of the present grid Sweden can supply 24%, Germany 60% and UK 21% of their current annual net electricity consumption from residential solar photovoltaic. In addition, we find that the grid-supported individual solar PV system sizes increase as population density decreases. Finally, our work highlights the importance of implementing sizing incentives for customers when installing their solar PV systems

Intelligent monitoring of the health and performance of distribution automation

With a move to 'smarter' distribution networks through an increase in distribution automation and active network management, the volume of monitoring data available to engineers also increases. It can be onerous to interpret such data to produce meaningful information about the health and performance of automation and control equipment. Moreover, indicators of incipient failure may have to be tracked over several hours or days. This paper discusses some of the data analysis challenges inherent in assessing the health and performance of distribution automation based on available monitoring data. A rule-based expert system approach is proposed to provide decision support for engineers regarding the condition of these components. Implementation of such a system using a complex event processing system shell, to remove the manual task of tracking alarms over a number of days, is discussed

Semantic web service architecture for simulation model reuse

COTS simulation packages (CSPs) have proved popular in an industrial setting with a number of software vendors. In contrast, options for re-using existing models seem more limited. Re-use of simulation component models by collaborating organizations is restricted by the same semantic issues however that restrict the inter-organization use of web services. The current representations of web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging semantic web. Semantic models, in the form of ontology, utilized by web service discovery and deployment architecture provide one approach to support simulation model reuse. Semantic interoperation is achieved through the use of simulation component ontology to identify required components at varying levels of granularity (including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The paper presents the development of ontology, connector software and web service discovery architecture in order to understand how such ontology are created, maintained and subsequently used for simulation model reuse. The ontology is extracted from health service simulation - comprising hospitals and the National Blood Service. The ontology engineering framework and discovery architecture provide a novel approach to inter- organization simulation, uncovering domain semantics and adopting a less intrusive interface between participants. Although specific to CSPs the work has wider implications for the simulation community

A Web Services Component Discovery and Deployment Architecture for Simulation Model Reuse

CSPs are widely used in industry, although have yet to operate across organizational boundaries. Reuse across organizations is restricted by the same semantic issues that restrict the inter-organization use of web services. The current representations of web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging semantic web. Semantic models, in the form of ontology, utilized by web service discovery and deployment architecture provide one approach to support simulation model reuse. Semantic interoperation is achieved through the use of simulation component ontology to identify required components at varying levels of granularity (including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The paper presents the development carried out within CSPI-PDG and Fluidity Group at Brunel University, of an ontology, connector software and web service discovery architecture. The ontology is extracted from simulation scenarios involving airport, restaurant and kitchen service suppliers. The ontology engineering framework and discovery architecture provide a novel approach to inter-organization simulation, adopting a less intrusive interface between participants. Although specific to CSPs the work has wider implications for the simulation community

Biologically Plausible Connectionist Prediction of Natural Language Thematic Relations

In Natural Language Processing (NLP) symbolic systems, several linguistic phenomena, for instance, the thematic role relationships between sentence constituents, such as AGENT, PATIENT, and LOCATION, can be accounted for by the employment of a rule-based grammar. Another approach to NLP concerns the use of the connectionist model, which has the benefits of learning, generalization and fault tolerance, among others. A third option merges the two previous approaches into a hybrid one: a symbolic thematic theory is used to supply the connectionist network with initial knowledge. Inspired on neuroscience, it is proposed a symbolic-connectionist hybrid system called BIO theta PRED (BIOlogically plausible thematic (theta) symbolic-connectionist PREDictor), designed to reveal the thematic grid assigned to a sentence. Its connectionist architecture comprises, as input, a featural representation of the words (based on the verb/noun WordNet classification and on the classical semantic microfeature representation), and, as output, the thematic grid assigned to the sentence. BIO theta PRED is designed to ""predict"" thematic (semantic) roles assigned to words in a sentence context, employing biologically inspired training algorithm and architecture, and adopting a psycholinguistic view of thematic theory.Fapesp - Fundacao de Amparo a Pesquisa do Estado de Sao Paulo, Brazil[2008/08245-4

Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection

Currently, power distribution companies have several problems that are related to energy losses. For example, the energy used might not be billed due to illegal manipulation or a breakdown in the customer’s measurement equipment. These types of losses are called non-technical losses (NTLs), and these losses are usually greater than the losses that are due to the distribution infrastructure (technical losses). Traditionally, a large number of studies have used data mining to detect NTLs, but to the best of our knowledge, there are no studies that involve the use of a Knowledge-Based System (KBS) that is created based on the knowledge and expertise of the inspectors. In the present study, a KBS was built that is based on the knowledge and expertise of the inspectors and that uses text mining, neural networks, and statistical techniques for the detection of NTLs. Text mining, neural networks, and statistical techniques were used to extract information from samples, and this information was translated into rules, which were joined to the rules that were generated by the knowledge of the inspectors. This system was tested with real samples that were extracted from Endesa databases. Endesa is one of the most important distribution companies in Spain, and it plays an important role in international markets in both Europe and South America, having more than 73 million customers

Ontology engineering for simulation component reuse

Commercial-off-the-shelf (COTS) simulation packages (CSPs) are widely used in industry, although they have yet to operate across organizational boundaries. Reuse across organizations is restricted by the same semantic issues that restrict the inter-organizational use of web services. The current representations of web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging semantic web. Semantic models, in the form of ontology, utilized by web service discovery and deployment architectures provide one approach to support simulation model reuse. Semantic interoperation is achieved through the use of simulation component ontologies to identify required components at varying levels of granularity (including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The paper presents the development of an ontology, connector software and web service discovery architecture. The ontology is extracted from simulation scenarios involving airport, restaurant and kitchen service suppliers. The ontology engineering framework and discovery architecture provide a novel approach to inter-organizational simulation, adopting a less intrusive interface between participants. Although specific to CSPs the work has wider implications for the simulation community

A simple maximum power point tracking based control strategy applied to a variable speed squirrel cage induction generator

This paper presents a comprehensive modelling and control study of a variable speed wind energy conversion system based on a squirrel-cage induction generator (SCIG). The mathematical model of the SCIG is derived in Park frame along with the indirect field oriented control (IFOC) scheme based on a proportional and integral speed controller. A simple maximum power point tracking strategy is used to determine the optimal speed under variable wind speed conditions which is then used as the reference in the IFOC scheme. Power flow between the supply and the inverter is regulated via simultaneous control of the active and reactive currents of the grid and the DC link voltage. The simulation results show that the proposed control technique is able to maximise the energy extracted from the wind during the simulation scenarios considered. The results also demonstrate good transient response characteristics in the decoupled real and reactive powers.Peer reviewedFinal Accepted Versio