Models are tools that help researchers to understand energy related processes while also providing policy makers with useful information for decision making. There are different kinds of models and they can be used for different purposes. Some models attempt to gauge the relationship of energy production-consumption to the prevailing price of energy. In other cases, models can be used to generate information about the rate of energy consumption against development for specific regions or groups of regions.
Environmental models might assess potential energy development together with environmental paramters, thereby serving to mitigate impacts, for example. Models can be simple, running quickly and for short periods of time, or they can be used over longer periods of time and incorporate higher levels of complexity with more variables. Digital economy and infrastructure enables greater levels of modelling. Interestingly, in areas where less digital data is possible can result in less modelling, and that impacts the kinds of models that can be used.
Modelling has grown in popularity. That growth is supported through increased knowledge about modeling processes but also attributable to higher levels of computerization, enabling digital infrastructure and policy issues demanding higher levels of accountability. In pactice models are used to understand processes better, and that has major benefits to the energy industry where more information translates into less risk. The implications of accurate modelling are real and can have significant impacts – reducing environmental impacts and to reducing safety risks, for example. But models are used for energy production, consumption and operations.
The United Kingdom recently published information about Energy Systems which is available through the UK Energy Research Centre. When considering energy that agency considers E4 modeling (energy-economic-engineering-environment). The purpose of this approach is toward integrating four factors into the development of overall energy research – a systems approach. This principle closely aligns with urban planning, for example, where multi-dimensional approaches are similarly used within a system structure. The idea here is to develop suitable (and sustainable) energy directions with a view to each of these factors. Clearly, if the production exceeds engineering capabilities then risk results, for example. Alternatively, it does not make much economic sense to impact a landscape that results in expensive consequences to remediate and reclaim. There is a balance to be established, but to achieve such balance, in some locations, or under specific conditions, may require modelling.The models would evaluate several factors so that the appropriate decision can be made.
Models are driven by data. It goes with out saying that models which seek to provide answers based upon little data are less reliable than those models that include more data. Geological information, seismic, sonar, wind and solar data are all useful for driving models. To enable appropriate tidal power energy development requires an understanidng of the tidal and wave conditions of the region where development is being considered. The European Marine Energy Centre has been involved in modelling tidal and wave energy for a considerable period of time.
While most energy related modelling was previously focused upon production of energy alone, today such models are more broadly based and can be found across independent company’s and throughout the energy sector covering business to production to energy transport, for example. The spread of energy related modelling has increased, driven in part by advances in energy technologies that produce data used to quantify processes, but also because of demands originating from policy factors.
Since the data created and used in energy modelling is digital, then advances in IT infrastructure have enabled the development of more complex modelling while growing a never ending thirst for more data as demands and expectations prior to decision making grow.The movement of this digital information is important since it connects the producers and users of modelling information in closer collaboration with decision makers. Accordingly, where telecommunications infrastructure supports digital data delivery – and modelling – then higher levels of modelling and the complexity of the modeling increases, particularly where real-time events are occuring and being modelled. We often refer to these as ‘mission critical’ systems and processes and they usually entail higher levels of authentification and security.
In most cases, modelling is uncoupled from live systems. That is, the data is collected, managed and processed for modelling. But the change in modelling over time has meant increasing amounts of real-time modeling. This also leads to a requirement for computing software and processing mechanisms that can integrate data suitable for modelling purposes, often pre-processing the information or processing it in an intermediary fashion prior to connecting to larger processing systems, often located on specialised servers and hardware.
Significant change has taken place in terms of the types of models, their complexity and who can use them. It is not out of the ordinary for competing agencies to be developing models based on similar portions of data derived through a common source. At the same time, these parties may be supplementing that information with individual developed and proprietary information created and collected through their own resources.
At the same time, the rise in tools for use to develop models has resulted in efficiencies that enable more people to operate them. Consequently, it would not be unusual for opposing forces to base their interpretations and goals on different approaches using similar tools.
So what’s the catch? Clearly, investment in data for modeling is important. Today’s tools can yield highly important, often critical results, that might not otherwise be apparent or available. Modelling enables enterprises with the ability to compete more effectively and completely while reducing risk and vulnerability.
Can a model be wrong? Yes, but usually weaker modelling is connected with the wrong types of information being used, or too little data driving the particular model for which specific answers are being sought. It is wise to use models as information sources, and to assign value to them based upon performance over time. But it is worth noting that energy models are increasingly connecting toward governance and consumers. This may be attributable to the fact that energy conservation and efficiency is most closely aligned to energy use and people making better decisions about how they use energy.