MODAM: A MODular Agent-based Modelling framework

Designing the smart grid requires combining varied models. As their number increases, so does the complexity of the software. Having a well thought architecture for the software then becomes crucial. This paper presents MODAM, a framework designed to combine agent-based models in a flexible and extensible manner, using well known software engineering design solutions (OSGi specification [1] and Eclipse plugins [2]). Details on how to build a modular agent-based model for the smart grid are given in this paper, illustrated by an example for a small network.

Building adaptable agent-based models – application to the electricity distribution network

Agent-based modelling (ABM), like other modelling techniques, is used to answer specific questions from real world systems that could otherwise be expensive or impractical. Its recent gain in popularity can be attributed to some degree to its capacity to use information at a fine level of detail of the system, both geographically and temporally, and generate information at a higher level, where emerging patterns can be observed. This technique is data-intensive, as explicit data at a fine level of detail is used and it is computer-intensive as many interactions between agents, which can learn and have a goal, are required. With the growing availability of data and the increase in computer power, these concerns are however fading. Nonetheless, being able to update or extend the model as more information becomes available can become problematic, because of the tight coupling of the agents and their dependence on the data, especially when modelling very large systems. One large system to which ABM is currently applied is the electricity distribution where thousands of agents representing the network and the consumers’ behaviours are interacting with one another. A framework that aims at answering a range of questions regarding the potential evolution of the grid has been developed and is presented here. It uses agent-based modelling to represent the engineering infrastructure of the distribution network and has been built with flexibility and extensibility in mind. What distinguishes the method presented here from the usual ABMs is that this ABM has been developed in a compositional manner. This encompasses not only the software tool, which core is named MODAM (MODular Agent-based Model) but the model itself. Using such approach enables the model to be extended as more information becomes available or modified as the electricity system evolves, leading to an adaptable model. Two well-known modularity principles in the software engineering domain are information hiding and separation of concerns. These principles were used to develop the agent-based model on top of OSGi and Eclipse plugins which have good support for modularity. Information regarding the model entities was separated into a) assets which describe the entities’ physical characteristics, and b) agents which describe their behaviour according to their goal and previous learning experiences. This approach diverges from the traditional approach where both aspects are often conflated. It has many advantages in terms of reusability of one or the other aspect for different purposes as well as composability when building simulations. For example, the way an asset is used on a network can greatly vary while its physical characteristics are the same – this is the case for two identical battery systems which usage will vary depending on the purpose of their installation. While any battery can be described by its physical properties (e.g. capacity, lifetime, and depth of discharge), its behaviour will vary depending on who is using it and what their aim is. The model is populated using data describing both aspects (physical characteristics and behaviour) and can be updated as required depending on what simulation is to be run. For example, data can be used to describe the environment to which the agents respond to – e.g. weather for solar panels, or to describe the assets and their relation to one another – e.g. the network assets. Finally, when running a simulation, MODAM calls on its module manager that coordinates the different plugins, automates the creation of the assets and agents using factories, and schedules their execution which can be done sequentially or in parallel for faster execution. Building agent-based models in this way has proven fast when adding new complex behaviours, as well as new types of assets. Simulations have been run to understand the potential impact of changes on the network in terms of assets (e.g. installation of decentralised generators) or behaviours (e.g. response to different management aims). While this platform has been developed within the context of a project focussing on the electricity domain, the core of the software, MODAM, can be extended to other domains such as transport which is part of future work with the addition of electric vehicles.

The greatest shadow on Earth

In a total solar eclipse, the Moon completely covers the Sun, casting a shadow several hundred km wide across the face of the Earth. This paper describes observations of the 14 November 2012 total eclipse of the Sun visible from north Queensland, Australia. The edge of the umbra was captured on video during totality, and this video is provided for teaching purposes. A series of simple 'kitchen' experiments are described which demonstrate the 'sunset' effect seen on the horizon during a total solar eclipse and also the curved umbra seen in the sky when the eclipsed Sun is relatively close to the horizon.

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.

Characterisation of lignins isolated from sugarcane bagasse pretreated with acidified ethylene glycol and ionic liquids

Sugarcane bagasse pretreatment processes using acidified aqueous ethylene glycol (EG) and ionic liquids (ILs) have been reported recently. In this study, recovery of lignins from these processes was conducted, as well as determination of their physico-chemical properties. The amount of lignins recovered from 1-butyl-3-methylimidazolium chloride ([bmim]Cl) with HCl as a catalyst and [bmim][CH3SO3] was ∼42%, and ∼35%–36% by EG with HCl or H2SO4 as a catalyst, respectively. The isolated lignins were characterised using wet chemistry, spectroscopy and thermogravimetry analysis (TGA), and the results compared to soda lignin from NaOH pretreatment of bagasse. The IL and EG lignins contained no or trace amounts of carbohydrates, slightly lower hydrogen content but slightly higher oxygen contents than soda lignin. The IL and EG lignins contained more C-3 and C-5 reactive sites for Mannich reaction and had more p-hydroxypheny propane unit structures than soda lignin. Two-dimensional heteronuclear single quantum coherence (2D HSQC) nuclear magnetic resonance (NMR) identified the major substructural units in the lignins, and allowed differences among them to be studied. As EG lignins were extracted in very reactive environment, intermediate enol ethers were formed and led to cleavage reactions which were not apparent in the other lignins. 31P NMR and infra-red spectroscopy results showed that IL and EG lignins had lower total hydroxyl content than soda lignin, probably indicating that a higher degree of self-polymerisation occurred during bagasse pretreatment, despite the use of lower temperature and shorter reaction time. On the basis of the salient features of these lignins, potential applications were proposed.

In the red shadow of the Earth

A technique is described for calculating the brightness of the atmosphere of the Earth that shines into the Earth’s umbra during a total lunar eclipse making the Moon red. This ‘Rim of Fire’ is due to refracted un scattered light from all the sunrises and sunsets rimming the Earth. In this article, a photograph of the totally eclipsed Moon was compared with the Full Moon and the difference in brightness calculated taking into account the exposure time and ISO setting. The results show that the Full Moon is over 14 000 times brighter than the totally eclipsed Moon. The relative brightness of the eclipsed Moon can be used to estimate that the luminance of Rim of Fire is over 12 trillion watts. The experiment described in this paper would be suitable as a high school or university exercise.