Performance evaluation of MPLS-enabled communications infrastructure for wide area monitoring systems

In order to obtain the transient power system measurement information, Wide Area Monitoring Systems (WAMS) should be able to collect Phasor Measurement Unit (PMU) data in a timely manner. Therefore along with the continual deployment of PMUs in Great Britain (GB) transmission system substations, a high performance communications infrastructure is becoming essential with regard to the establishment of reliable WAMS. This paper focuses mainly on evaluating the performance of the real-time WAMS communication infrastructure when Multi-Protocol Label Switching (MPLS) capability is added to a conventional IP network. Furthermore, PMU communications from geographically distributed substations to a Phasor Data Concentrator (PDC) are investigated over different transport protocols. Using OPNET Modeler, simulations are performed based on the existing WAMS infrastructure as installed on the GB transmission system. The simulation results are analyzed in detail in order to fully determine the different characteristics of communication delays between PMUs and PDC

A multi-agent model for assessing electricity tariffs

This paper describes the framework for modelling a multi-agent approach for assessing dynamic pricing of electricity and demand response. It combines and agent-based model with decision-making data, and a standard load-flow model. The multi-agent model described here represents a tool in investigating not only the relation between different dynamic tariffs and consumer load profiles, but also the change in behaviour and impact on low-voltage electricity distribution networks.The authors acknowledge the contribution of the EPSRC Transforming Energy Demand Through Digital Innovation Programme, grant agreement numbers EP/I000194/1 and EP/I000119/1, to the ADEPT project

Sustainable Energy - Technological Issues, Applications and Case Studies

The sustainable energy sources are potentially employed to substitute petrol fuels in transport engines such as buses and small vehicles. Hydrogen-enriched compressed natural gas engines are forthcoming energy carriers for the internal combustion engine, with higher thermal efficiency and less pollutant emissions. The different availability of renewables has allowed various countries to adopt the most appropriate type of renewable energy technology according to their energy source adequacy/abundance. In Taiwan, ocean energy is considered as an abundant source of renewables due to its geographical location as an island. The Taiwanese government has approved the investment to construct an MW-scale demonstration electricity plant. In this book, the Taiwanese ocean energy experience is comprehensively presented. The technical and legal analyses of ocean energy implementation are provided. The challenges that they had to overcome to optimize the utilization of the most available ocean energy potential are discussed. The sustainable transition in South Africa would be a good example for implementing rooftop solar, especially in low-income communities. Apart from the environmental benefits, sustainable energy technologies can boost the socioeconomic level of developing countries. Other advantages may be the continuous supply of energy and creation of new job opportunities. Moreover, sustainable renewable energy sources such as the wind could be employed for generating electricity to operate water purification systems in remote areas. This, in turn, would overcome the health problems associated with drinking water scarcity issues. This book is an attempt to cover the sustainable energy issues from a technical perspective. Furthermore, the sustainable energy applications and existing case studies are helpful illustrations for the broad understanding of the importance of sustainable energy

Parallel detrended fluctuation analysis for fast event detection on massive PMU data

("(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")Phasor measurement units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronized measurements. The devices high data reporting rates present major computational challenges in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain from the aspects of speedup, scalability, and accuracy. The speedup of the PDFA in computation is initially analyzed through Amdahl's Law. A revision to the law is then proposed, suggesting enhancements to its capability to analyze the performance gain in computation when parallelizing data intensive applications in a cluster computing environment

Parallel detrended fluctuation analysis for fast event detection on massive PMU data

("(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")Phasor measurement units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronized measurements. The devices high data reporting rates present major computational challenges in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain from the aspects of speedup, scalability, and accuracy. The speedup of the PDFA in computation is initially analyzed through Amdahl's Law. A revision to the law is then proposed, suggesting enhancements to its capability to analyze the performance gain in computation when parallelizing data intensive applications in a cluster computing environment

Performance of grid-connected solar photovoltaic systems with single-tuned and double-tuned harmonic passive filters

The generated solar photovoltaic power can be stand-alone or grid-connected. In both systems, power quality issues arise and can affect the network. The harmonic distortions can affect the system significantly if they are not mitigated. This paper presents the performance of grid-connected solar photovoltaic systems with single-tuned and double-tuned filters for harmonics mitigation. The design aspects of each filter are presented and discussed. The simulation results are analyzed and validated using ETAP software

Lessons learnt from mining meter data of residential consumers

Tracking end-users' usage patterns can enable more accurate demand forecasting and the automation of demand response execution. Accordingly, more advanced applications, such as electricity market design, integration of distributed generation and theft detection can be developed. By employing data mining techniques on smart meter recordings, the suppliers can efficiently investigate the load patterns of consumers. This paper presents applications where data mining of energy usage can derive useful information. Higher demands, on one side, and the energy price increase on the other side, have caused serious issues with regards to electricity theft, especially among developing countries. This phenomenon leads to considerable operational losses within the electrical network. In order to identify illegal residential consumers, a new method of analysing and identifying electricity consumption patterns of consumers is proposed in this paper. Moreover, the importance of data mining for analysing the consumer's usage curves was investigated. This helps to determine the behaviour of end-users for demand response purposes and improve the reliability and security of the electricity network. Clustering load profiles for large scale energy datasets are discussed in detail

Study of Electric Vehicles for Grid Services-A Gender-Based Approach

This paper investigates the battery degradation and financial benefits of using an EV as a car only and as a revenue source as well when participating in the electric grid services. The study looks into behaviours of female and male drivers to investigate whether gender plays an important role in battery degradation of EVs. Firstly, a battery degradation model is developed using data provided by an EV maker. Then, a breakeven point study is presented to compare between an EV and an internal combustion engine (ICE) vehicle cost of ownership, differentiated by usage type and gender. It is shown that if the difference in price is above 5000 GBP and the milage is low, EVs are less attractive, unless used for grid support. The results show that a single weekly grid support event is not harmful for the EV battery life but has a great impact on the breakeven point and makes the EV a competitor to the ICE vehicle which facilities the future transition to a market full of EVs. Battery degradation considering published chemistry and performance data differs very slightly between male and female users.10.13039/501100000266-EPSR

A stochastic model for estimating electric vehicle arrival at multi-charger forecourts

Data availability: Data will be made available on request.Copyright © 2022 The Author(s). Many countries are observing significant growth rates in electric vehicle (EV) uptake, often backed by financial incentives or regulation and legislation. The availability of large multi-charger sites for rapid EV charging with an experience similar to conventional refueling refuelling stations lowers the barrier to acceptance for drivers considering the switch to using an EV. The question arises about how to size such a facility at the design and planning stage, as well as accommodating growth in the number of EVs in daily use. One of the important factors is the vehicle arrival rate and the corresponding power and energy demand. EV charging is a function of several parameters, all of which are stochastic in nature, such as the vehicle daily travelled distance, charging start time and the required energy. To account for uncertainty in the parameters, a stochastic model has been designed to simulate realistic vehicle arrival rates. The model accounts for EVs coming from the site catchment area and opportunistic charging from passing traffic traveling travelling on the major roads adjacent to the site, the seasonality of parameters, and charging at places other than the site (competitive charging). The model produced plausible EV arrival patterns for both local and passing traffic, and reproduced the characteristic power demand at the case study site. All estimates incorporate uncertainty, reflecting realistic variability of the important parameters. The model in independent of location, uses open-source data, and is structured  flexibly, making it adaptable to new sites as part of the technical and business planning process.UK Research Council (UKRI) through Innovate UK under grant No. TS/T006471/1