Residential demand management using individualised demand aware price policies

This paper presents a novel approach to Demand Side Management (DSM), using an “individualised” price policy, where each end user receives a separate electricity pricing scheme designed to incentivise demand management in order to optimally manage flexible demands. These pricing schemes have the objective of reducing the peaks in overall system demand in such a way that the average electricity price each individual user receives is non-discriminatory. It is shown in the paper that this approach has a number of advantages and benefits compared to traditional DSM approaches. The “demand aware price policy” approach outlined in this paper exploits the knowledge, or demand-awareness, obtained from advanced metering infrastructure. The presented analysis includes a detailed case study of an existing European distribution network where DSM trial data was available from the residential end-users

Student Authentication for Oral Assessment in Distance Learning Programs

The past decade has seen the proliferation of e-learning and distance learning programs across a wealth of discipline areas. In order to preserve maximum flexibility in outreach, student assessment based exclusively on remotely submitted work has become commonplace. However, there is also growing evidence that e-learning also provides increased opportunity for plagiarism with obvious consequences for learning effectiveness. This paper reports on the development of a prototype student authentication system designed for use with a graduate e-learning program. The proposed system can be used to authenticate a telephone-based oral examination which can, in turn, be used to confirm a student’s ability in relation to submitted assignments and online test results. The prototype low-cost system is shown to be sufficiently accurate to act as an effective deterrent against plagiarism

Ontology Summit 2008 Communiqué: Towards an open ontology repository

Each annual Ontology Summit initiative makes a statement appropriate to each Summits theme as part of our general advocacy designed to bring ontology science and engineering into the mainstream. The theme this year is "Towards an Open Ontology Repository". This communiqué represents the joint position of those who were engaged in the year's summit discourse on an Open Ontology Repository (OOR) and of those who endorse below. In this discussion, we have agreed that an "ontology repository is a facility where ontologies and related information artifacts can be stored, retrieved and managed." We believe in the promise of semantic technologies based on logic, databases and the Semantic Web, a Web of exposed data and of interpretations of that data (i.e., of semantics), using common standards. Such technologies enable distinguishable, computable, reusable, and sharable meaning of Web and other artifacts, including data, documents, and services. We also believe that making that vision a reality requires additional supporting resources and these resources should be open, extensible, and provide common services over the ontologies

Designing laboratory experiments for electricity grid integration of renewable energy using microgrid, test-rig emulators and real time simulation tools.

This paper describes efforts to integrate advanced approaches in microgrid, test-rig emulators and real time simulation into early postgraduate and undergraduate engineering education. It describes two experiments designed for groups of early stage researchers and postgraduate students in the field of Offshore Renewable Energy (ORE). These electrical laboratory experiments are part of a H2020-funded weeklong training course and focus on a medium speed rotary emulator for wave energy applications, and a wind turbine emulator that demonstrated the operation of a Doubly-Fed Induction Generator (DFIG) in a two-machine coupled rig. This paper also discusses some initial reviews of the training course. These reviews noted that students had a desire for more hands-on experimental work, and that the requirements to cover electrical safety material limited the amount of effective time available for experimental work. Finally, the paper outlines some approaches for improving the design of future laboratory experiments in this area

User flexibility aware price policy synthesis for smart grids

In order to optimally manage a modern electricity distribution network, peaks in residential users demand should be avoided, as this can reduce energy and network asset management costs. Furthermore, this must be done without compressing residential users demand. To this aim, in a demand response setting, residential users are given a price policy, which economically motivates them to shift their loads in order to achieve this goal. However, if the price policy for all users is similar, this demand response may result in simply shifting the demand peaks (peak rebound), leaving the problem unsolved. In this paper we propose a novel methodology which i) for each network substation s, automatically computes the desired power profile to be kept in order to optimally manage the network itself, ii) for each network substation s, automatically synthesizes individualized price policies for residential users connected to s, so that s is kept at the desired profile. Note that price policies individualization avoids the peak rebound problem, as different users have different low tariff areas. Furthermore, our methodology measures the flexibility of a residential user as the capacity needed by a home energy storage system (e.g., a battery) to always follow the given price policy, thus mitigating residential users discomfort. We show the feasibility of our approach on a realistic scenario taken from an existing medium voltage Danish distribution network

Network Operators Advice and Assistance (NOAA): a real-time traffic rerouting expert system

A real-time autonomous expert system has been developed to carry out traffic management in the Southern Californian telephone network. The system has been working on live data since September 1991 and generates rerouting advice that agrees with that generated by the present network management procedures. A modular software design was adopted to allow for evolution. A graphics interface allows the user to easily navigate through the display of exception conditions and advice. Exceptions are shown highlighted on a map of Southern California. A severity measure is calculated for each exception and is used to prioritize the display of information

Parallel statistical model checking for safety verification in smart grids

By using small computing devices deployed at user premises, Autonomous Demand Response (ADR) adapts users electricity consumption to given time-dependent electricity tariffs. This allows end-users to save on their electricity bill and Distribution System Operators to optimise (through suitable time-dependent tariffs) management of the electric grid by avoiding demand peaks. Unfortunately, even with ADR, users power consumption may deviate from the expected (minimum cost) one, e.g., because ADR devices fail to correctly forecast energy needs at user premises. As a result, the aggregated power demand may present undesirable peaks. In this paper we address such a problem by presenting methods and a software tool (APD-Analyser) implementing them, enabling Distribution System Operators to effectively verify that a given time-dependent electricity tariff achieves the desired goals even when end-users deviate from their expected behaviour. We show feasibility of the proposed approach through a realistic scenario from a medium voltage Danish distribution network

Use of Inverse Reinforcement Learning for Identity Prediction

We adopt Markov Decision Processes (MDP) to model sequential decision problems, which have the characteristic that the current decision made by a human decision maker has an uncertain impact on future opportunity. We hypothesize that the individuality of decision makers can be modeled as differences in the reward function under a common MDP model. A machine learning technique, Inverse Reinforcement Learning (IRL), was used to learn an individual's reward function based on limited observation of his or her decision choices. This work serves as an initial investigation for using IRL to analyze decision making, conducted through a human experiment in a cyber shopping environment. Specifically, the ability to determine the demographic identity of users is conducted through prediction analysis and supervised learning. The results show that IRL can be used to correctly identify participants, at a rate of 68% for gender and 66% for one of three college major categories

Emergence of the L phenotype in Group B Streptococci in the South of Ireland

Group B Streptococcal isolates (n = 235) from the South of Ireland were characterised by serotyping, antimicrobial susceptibility and determination of the phenotypic and genotypic mechanisms of resistance. Resistance to erythromycin and clindamycin was observed in 21·3% and 20·4% of the total population, respectively. The c-MLSB phenotype was the most common phenotype detected (62%), with ermB being the predominant genetic determinant, present in 84% of resistant isolates. The rare L phenotype was observed in 2·9% (n = 7) of isolates, four of which harboured the lsaC gene responsible for clindamycin resistance. Serotypes Ia, III and II were the most common amongst the entire study population (28·1%, 24·7% and 14%, respectively). Four of the seven L phenotype isolates were serotype III and two of these strains were confirmed as the hypervirulent clone, ST-17 and harboured the hvgA gene. This is the first documented case of the L phenotype in Ireland to date and the study findings emphasise the need for continued monitoring of antibiotic resistance and serotype distribution in GBS isolates from Ireland

An Overview of Demand Response : From its Origins to the Smart Energy Community

The need to improve power system performance, enhance reliability, and reduce environmental effects, as well as advances in communication infrastructures, have led to demand response (DR) becoming an essential part of smart grid operation. DR can provide power system operators with a range of flexible resources through different schemes. From the operational decision-making viewpoint, in practice, each scheme can affect the system performance differently. Therefore, categorizing different DR schemes based on their potential impacts on the power grid, operational targets, and economic incentives can embed a pragmatic and practical perspective into the selection approach. In order to provide such insights, this paper presents an extensive review of DR programs. A goal-oriented classification based on the type of market, reliability, power flexibility and the participants’ economic motivation is proposed for DR programs. The benefits and barriers based on new classes are presented. Every involved party, including the power system operator and participants, can utilize the proposed classification to select an appropriate plan in the DR-related ancillary service ecosystem. The various enabling technologies and practical strategies for the application of DR schemes in various sectors are reviewed. Following this, changes in the procedure of DR schemes in the smart community concept are studied. Finally, the direction of future research and development in DR is discussed and analyzed.© 2021 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed