Impact of Heat Pump Load on Distribution Networks

Heat pumps can provide domestic heating at a cost that is competitive with oil heating in particular. If the electricity supply contains a significant amount of renewable generation, a move from fossil fuel heating to heat pumps can reduce greenhouse gas emissions. The inherent thermal storage of heat pump installations can also provide the electricity supplier with valuable flexibility. The increase in heat pump installations in the UK and Europe in the last few years poses a challenge for low-voltage networks, due to the use of induction motors to drive the pump compressors. The induction motor load tends to depress voltage, especially on starting. The paper includes experimental results, dynamic load modelling, comparison of experimental results and simulation results for various levels of heat pump deployment. The simulations are based on a generic test network designed to capture the main characteristics of UK distribution system practice. The simulations employ DIgSlILENT to facilitate dynamic simulations that focus on starting current, voltage variations, active power, reactive power and switching transients.

STPA-SafeSec: Safety and Security Analysis for Cyber-Physical Systems

Cyber-physical systems tightly integrate physical processes and information and communication technologies. As today’s critical infrastructures, e.g., the power grid or water distribution networks, are complex cyber-physical systems, ensuring their safety and security becomes of paramount importance. Traditional safety analysis methods, such as HAZOP, are ill-suited to assess these systems. Furthermore, cybersecurity vulnerabilities are often not considered critical, because their effects on the physical processes are not fully understood. In this work, we present STPA-SafeSec, a novel analysis methodology for both safety and security. Its results show the dependencies between cybersecurity vulnerabilities and system safety. Using this information, the most effective mitigation strategies to ensure safety and security of the system can be readily identified. We apply STPA-SafeSec to a use case in the power grid domain, and highlight its benefits.

Identification of Microturbine Model for Long-term Dynamic Analysis of Distribution Networks

As one of the most successfully commercialized distributed energy resources, the long-term effects of microturbines (MTs) on the distribution network has not been fully investigated due to the complex thermo-fluid-mechanical energy conversion processes. This is further complicated by the fact that the parameter and internal data of MTs are not always available to the electric utility, due to different ownerships and confidentiality concerns. To address this issue, a general modeling approach for MTs is proposed in this paper, which allows for the long-term simulation of the distribution network with multiple MTs. First, the feasibility of deriving a simplified MT model for long-term dynamic analysis of the distribution network is discussed, based on the physical understanding of dynamic processes that occurred within MTs. Then a three-stage identification method is developed in order to obtain a piecewise MT model and predict electro-mechanical system behaviors with saturation. Next, assisted with the electric power flow calculation tool, a fast simulation methodology is proposed to evaluate the long-term impact of multiple MTs on the distribution network. Finally, the model is verified by using Capstone C30 microturbine experiments, and further applied to the dynamic simulation of a modified IEEE 37-node test feeder with promising results.

Integrated Optimal Power Flow for Distribution Networks in Local and Urban Scales

This paper develops an integrated optimal power flow (OPF) tool for distribution networks in two spatial scales. In the local scale, the distribution network, the natural gas network, and the heat system are coordinated as a microgrid. In the urban scale, the impact of natural gas network is considered as constraints for the distribution network operation. The proposed approach incorporates unbalance three-phase electrical systems, natural gas systems, and combined cooling, heating, and power systems. The interactions among the above three energy systems are described by energy hub model combined with components capacity constraints. In order to efficiently accommodate the nonlinear constraint optimization problem, particle swarm optimization algorithm is employed to set the control variables in the OPF problem. Numerical studies indicate that by using the OPF method, the distribution network can be economically operated. Also, the tie-line power can be effectively managed.

A new approach for the detection of ethylene using silica-supported palladium complexes

The coordination of olefins to square-planar Pd(II) and Pt(II) complexes containing 2,9-dimethylphenanthroline (L1) often involves a change of color associated with a change of geometry at the metal center. In order to obtain suitable colorimetric detectors for ethylene gas, a series of new Pd(II) and Pt(II) compounds with a range of 2,9-disubstituted phenanthroline ligands [2,9-di-n-butyl-1,10-phenanthroline (L-2), 2,9-di-s-butyl-1,10-phenanthroline (L3), 2,9-diphenyl-1,10-phenanthroline (L4), and 2,9-dimethyl-4,7-diphenyl-1,10-phenanthroline (bathocuproine, L5)] have been prepared and their reactivity toward ethylene investigated both in solution and after depositing the detector compounds on a variety of solid supports. The Pd(II) complex [PdCl2(L2)] supported on silica undergoes a clear color change upon exposure to ethylene, while remaining stable toward air and water, and forms the basis for new simple colorimetric detectors with potential applications in ethylene pipe-leak detection and the monitoring of fruit ripening. Encouragingly, the detector is able to discriminate between fruit at different stages of ripening. The response of the detector to other volatiles was also examined, and specific color changes were also observed upon exposure to aromatic acetylenes. The crystal structures of four new derivatives, including the ethylene-Pt(II) complex [PtCl2(C2H4)(L2)], are also described.

The Area Distribution of Solar Magnetic Bright Points

Magnetic bright points (MBPs) are among the smallest observable objects on the solar photosphere. A combination of G-band observations and numerical simulations is used to determine their area distribution. An automatic detection algorithm, employing one-dimensional intensity profiling, is utilized to identify these structures in the observed and simulated data sets. Both distributions peak at an area of approximate to 45,000 km(2), with a sharp decrease toward smaller areas. The distributions conform with log-normal statistics, which suggests that flux fragmentation dominates over flux convergence. Radiative magneto-convection simulations indicate an independence in the MBP area distribution for differing magnetic flux densities. The most commonly occurring bright point size corresponds to the typical width of inter-granular lanes.

Development of ELISAs for detecting domoic acid, okadaic acid, and saxitoxin and their applicability for the detection of marine toxins in samples collected in Belgium

Okadaic acid, a diarrhetic shellfish poison, domoic acid, an amnesic shellfish poison, and saxitoxin, a paralytic shellfish poison, are three of the best-known marine biotoxins. The mouse bioassay is the method most widely used to detect many of these toxins in shellfish samples, but animal welfare concerns have prompted researchers to seek alternative methods of detection. In this study, three direct competitive enzyme-linked immunosorbent assays (ELISAs), each based on antibodies raised in rabbits against a conjugate of the analyte of interest, were developed for marine biotoxin detection in mussel, oyster, and scallop. One assay was for okadaic acid, one for saxitoxin, and one for domoic acid usually detected and quantified by high-performance liquid chromatography-ultraviolet light (HPLC-UV). All three compounds and a number of related toxins were extracted quickly and simply from the shellfish matrices with a 9 : 1 mixture of ethanol and water before analysis. The detection capabilities (CC values) of the developed ELISAs were 150 mu g kg-1 for okadaic acid, 50 mu g kg-1 for domoic acid, and 5 mu g kg-1 or less for saxitoxin. The assays proved satisfactory when used over a 4-month period for the analysis of 110 real samples collected in Belgium.

Investigation of AIMD based charging strategies for EVs connected to a low-voltage distribution network

In this paper we consider charging strategies that mitigate the impact of domestic charging of EVs on low-voltage distribution networks and which seek to reduce peak power by responding to time-ofday pricing. The strategies are based on the distributed Additive Increase and Multiplicative Decrease (AIMD) charging algorithms proposed in [5]. The strategies are evaluated using simulations conducted on a custom OpenDSS-Matlab platform for a typical low voltage residential feeder network. Results show that by using AIMD based smart charging 50% EV penetration can be accommodated on our test network, compared to only 10% with uncontrolled charging, without needing to reinforce existing network infrastructure. © Springer-Verlag Berlin Heidelberg 2013.

Mode of prostate cancer detection is associated with the psychological wellbeing of survivors: results from the PiCTure study

Purpose: Many men with prostate cancer are asymptomatic, diagnosed following prostate specific antigen (PSA) testing. We investigate whether mode of detection, i.e. ‘PSA detected’ or ‘clinically detected’, was associated with psychological wellbeing among prostate cancer survivors. Methods: A cross-sectional postal questionnaire was administered in 2012 to 6559 prostate cancer (ICD10 C61) survivors up to 18 years post-diagnosis, identified through population-based cancer registries in Ireland. Psychological wellbeing was assessed using the Depression Anxiety Stress Scale-21. Logistic regression was used to investigate associations between mode of detection and depression, anxiety and stress, adjusting for socio-demographic and clinical confounders. Results: The response rate was 54 % (3348/6262). Fifty-nine percent of survivors were diagnosed with asymptomatic PSA-tested disease. Prevalence of depression (13.8 vs 20.7 %; p < 0.001), anxiety (13.6 vs 20.9 %; p < 0.001) and stress (8.7 vs 13.8 %; p < 0.001) were significantly lower among survivors diagnosed with PSA-detected, than clinically detected disease. After adjusting for clinical and socio-demographic factors, survivors with clinically detected disease had significantly higher risk of depression (odds ratio (OR) = 1.46 95 % CI 1.18, 1.80; p = 0.001), anxiety (OR = 1.36 95 % CI 1.09, 1.68; p = 0.006) and stress (OR = 1.43 95 % CI 1.11, 1.85; p = 0.006) than survivors with PSA-detected disease. Conclusions: These findings contribute to the ongoing debate on benefits and risks of PSA testing and may be considered by policy makers formulating population-based prostate cancer screening policies. The relatively high prevalence of negative psychological states among survivors means that a ‘risk-adapted approach’ should be implemented to screen survivors most at risk of psychological morbidity for psychological health, and mode of detection could be considered as a risk stratum.

Local Storage Meets Local Demand: A Technical Solution to Future Power Distribution System

Future power systems are expected to integrate large-scale stochastic and intermittent generation and load due to reduced use of fossil fuel resources, including renewable energy sources (RES) and electric vehicles (EV). Inclusion of such resources poses challenges for the dynamic stability of synchronous transmission and distribution networks, not least in terms of generation where system inertia may not be wholly governed by large-scale generation but displaced by small-scale and localised generation. Energy storage systems (ESS) can limit the impact of dispersed and distributed generation by offering supporting reserve while accommodating large-scale EV connection; the latter (load) also participating in storage provision. In this paper, a local energy storage system (LESS) is proposed. The structure, requirement and optimal sizing of the LESS are discussed. Three operating modes are detailed, including: 1) storage pack management; 2) normal operation; and 3) contingency operation. The proposed LESS scheme is evaluated using simulation studies based on data obtained from the Northern Ireland regional and residential network.

Knowledge-based risk assessment under uncertainty in engineering projects

In this paper we describe how an evidential-reasoner can be used as a component of risk assessment of engineering projects using a direct way of reasoning. Guan & Bell (1991) introduced this method by using the mass functions to express rule strengths. Mass functions are also used to express data strengths. The data and rule strengths are combined to get a mass distribution for each rule; i.e., the first half of our reasoning process. Then we combine the prior mass and the evidence from the different rules; i.e., the second half of the reasoning process. Finally, belief intervals are calculated to help in identifying the risks. We apply our evidential-reasoner on an engineering project and the results demonstrate the feasibility and applicability of this system in this environment.

Risk assessment of E-Commerce projects using evidential reasoning

The purpose of this study is to develop a decision making system to evaluate the risks in E-Commerce (EC) projects. Competitive software businesses have the critical task of assessing the risk in the software system development life cycle. This can be conducted on the basis of conventional probabilities, but limited appropriate information is available and so a complete set of probabilities is not available. In such problems, where the analysis is highly subjective and related to vague, incomplete, uncertain or inexact information, the Dempster-Shafer (DS) theory of evidence offers a potential advantage. We use a direct way of reasoning in a single step (i.e., extended DS theory) to develop a decision making system to evaluate the risk in EC projects. This consists of five stages 1) establishing knowledge base and setting rule strengths, 2) collecting evidence and data, 3) determining evidence and rule strength to a mass distribution for each rule; i.e., the first half of a single step reasoning process, 4) combining prior mass and different rules; i.e., the second half of the single step reasoning process, 5) finally, evaluating the belief interval for the best support decision of EC project. We test the system by using potential risk factors associated with EC development and the results indicate that the system is promising way of assisting an EC project manager in identifying potential risk factors and the corresponding project risks.