Stochastic Hosting Capacity in LV Distribution Networks

Hosting capacity is defined as the level of penetration that a particular technology can connect to a distribution network without causing power quality problems. In this work, we study the impact of solar photovoltaics (PV) on voltage rise. In most cases, the locations and sizes of the PV are not known in advance, so hosting capacity must be considered as a random variable. Most hosting capacity methods study the problem considering a large number of scenarios, many of which provide little additional information. We overcome this problem by studying only cases where voltage constraints are active, with results illustrating a reduction in the number of scenarios required by an order of magnitude. A linear power flow model is utilised for this task, showing excellent performance. The hosting capacity is finally studied as a function of the number of generators connected, demonstrating that assumptions about the penetration level will have a large impact on the conclusions drawn for a given network

Mitigating the Impact of Personal Vehicle Electrification: a Power Generation Perspective

The number of electric vehicles on the road in the UK is expected to rise quickly in the coming years, and this is likely to have an impact on the operation of the power grid. This paper first quantifies the consequences of allowing a completely electric fleet to charge freely, then considers whether there is a practical way in which the impacts can be mitigated. We predict that, with an entirely electric fleet, the UK power generation capacity would need to increase by 1/3. We show that it is possible to completely mitigate this with controlled charging, although substantial infrastructure would be required. However, we propose a simple scheme which could largely avoid the negative effect and does not require the creation of new infrastructure. We show that this reduces the projected increase in peak electricity demand by 80-99%

Evolution of natural risk: research framework and perspectives

International audienceThis study presents a conceptual framework for addressing temporal variation in natural risk. Numerous former natural risk analyses and investigations have demonstrated that time and related changes have a crucial influence on risk. For natural hazards, time becomes a factor for a number of reasons. Using the example of landslides to illustrate this point, it is shown that: 1. landslide history is important in determining probability of occurrence, 2. the significance of catchment variables in explaining landslide susceptibility is dependent on the time scale chosen, 3. the observer's perception of the geosystem's state changes with different time spans, and 4. the system's sensitivity varies with time. Natural hazards are not isolated events but complex features that are connected with the social system. Similarly, elements at risk and their vulnerability are highly dynamic through time, an aspect that is not sufficiently acknowledged in research. Since natural risk is an amalgam of hazard and vulnerability, its temporal behaviour has to be considered as well. Identifying these changes and their underlying processes contributes to a better understanding of natural risk today and in the future. However, no dynamic models for natural risks are currently available. Dynamic behaviour of factors affecting risk is likely to create increasing connectivity and complexity. This demands a broad approach to natural risk, since the concept of risk encapsulates aspects of many disciplines and has suffered from single-discipline approaches in the past. In New Zealand, dramatic environmental and social change has occurred in a relatively short period of time, graphically demonstrating the temporal variability of the geosystem and the social system. To understand these changes and subsequent interactions between both systems, a holistic perspective is needed. This contribution reviews available frameworks, demonstrates the need for further concepts, and gives research perspectives on a New Zealand example

Clustering of Usage Profiles for Electric Vehicle Behaviour Analysis

Accurately predicting the behaviour of electric vehicles is going to be imperative for network operators. In order for vehicles to participate in either smart charging schemes or providing grid services, their availability and charge requirements must be forecasted. Their relative novelty means that data concerning electric vehicles is scarce and biased, however we have been collecting data on conventional vehicles for many years. This paper uses cluster analysis of travel survey data from the UK to identify typical conventional vehicle usage profiles. To this end, we determine the feature vector, introduce an appropriate distance metric, and choose a number of clusters. Five clusters are identified, and their suitability for electrification is discussed. A smaller data set of electric vehicles is then used to compare the current electric fleet behaviour with the conventional one

Rethinking the Ken Through the Lens of Psychological Science

Canadian courts regularly exclude psychological expert evidence that would explain the factors that produce mistaken eyewitness identifications and false confessions (two significant sources of wrongful convictions). Courts justify these exclusions on the basis that the evidence is not beyond the ken of the trier of fact—the psychologist would simply be describing an experience shared by the judge and jury. In this article, the authors suggest this reasoning rests on two fundamental misunderstandings of psychology: unconscious neglect and dispositionism. In other words, judges mistakenly assume the trier of fact understands the unconscious situational forces that distort memories and cause innocent people to confess. Moreover, judges appear to prefer dispositional evidence of some disorder or syndrome suffered by the accused or by the witness to the crime. After demonstrating evidence of such reasoning in several decisions, the authors suggest reforms based on a more nuanced understanding of human psychology

Conic optimisation for electric vehicle station smart charging with battery voltage constraints

This paper proposes a new convex optimisation strategy for coordinating electric vehicle charging, which accounts for battery voltage rise, and the associated limits on maximum charging power. Optimisation strategies for coordinating electric vehicle charging commonly neglect the increase in battery voltage which occurs as the battery is charged. However, battery voltage rise is an important consideration, since it imposes limits on the maximum charging power. This is particularly relevant for DC fast charging, where the maximum charging power may be severely limited, even at moderate state of charge levels. First, a reduced order battery circuit model is developed, which retains the nonlinear relationship between state of charge and maximum charging power. Using this model, limits on the battery output voltage and battery charging power are formulated as second-order cone constraints. These constraints are integrated with a linearised power flow model for three-phase unbalanced distribution networks. This provides a new multiperiod optimisation strategy for electric vehicle smart charging. The resulting optimisation is a second-order cone program, and thus can be solved in polynomial time by standard solvers. A receding horizon implementation allows the charging schedule to be updated online, without requiring prior information about when vehicles will arrive