SATELLITE Workshop 5th September, 2012:
Sustainable Energy, Complexity Science and the Smart Grid
Workshop Rationale
One of the greatest problems that we face is that of maintaining the levels of comfort and quality of life in the face of a need to dramatically reduce our CO2 emissions and hence our dependency on fossil fuels. This is because the level of carbon emissions is linked to the amount of future Climate Change that we shall face and to the possibility of massive disruption and disaster if we do not reduce our use of fossil fuels. By 2050 we are supposed to have reduced our carbon emissions by 80% of the 1990 level, and this will require an enormous scientific and technological effort. Of course we can try to reduce our energy requirements with better insulation, less travel etc. but this really will not achieve anything like the decrease in emissions that are required. Generally the plan is to make electricity the main energy carrier, to include transport and heating, and to generate what will be around three times the amount of electricity as today, but with only 20% of the emissions of 1990. Obviously, this will entail the development of very large amounts of energy using ‘renewables’ such as wind, solar, tidal etc. as well as nuclear, and the large volumes of ‘renewable’ sources will introduce a far greater level of intermittency in the power generation system. In general then, the system will change from being essentially a ‘top-down’ centralized generation system, with distribution organized through a central network to a new spatially distributed pattern of generation and consumption, which will constitute a Complex System. Because of this the development of this new, vital infrastructure is being studied using complexity science.
In particular, the dynamic, spatially distributed patterns of generation, storage and consumption are being studied using multi-agent models, with learning agents at different levels of the system. So, there will be many generators of different sizes, and there will be dynamic markets of ‘aggregators’ who will distribute energy from multiple sources, providing resilience in the market place. Also, there will be the development of Smart grids that can use information to manage demand and supply dynamically, thus reducing the amount of extra capacity needed to deal with the intermittency of the renewable generation. This is of great interest from a Complexity Science standpoint to facilitate the development of the grid - at a task-oriented level of description - as a “system of systems”, which could co-evolve to replace the centralised structure of national power systems evident today. This conception of the Smart Grid is now of international interest and concern. From this perspective, there is a need to raise awareness of stakeholders at all levels of the possible emergent properties that may be expected from interactions between the individual systems. For example, systems of this kind can exhibit specific kinds of emergence that might be desirable such as resilience, or undesirable, such as instability, in this context. At another level of description, the Smart Grid has been conceptualised potentially as a Complex
Adaptive System (CAS), for example in studies such as CASCADE1, which aims to provide a framework for the investigation of these phenomena and their implications across technical, sociotechnical and socioeconomic domains as the CAS evolve over time.
The drivers of change vary in scale and intensity across national boundaries and some understanding of these is necessary to fully appreciate the issues. Aging infrastructure, capacity constraints and the need to reduce greenhouse gas and other emissions are the most obvious drivers but more complex issues have arisen from deregulation in many countries. This has resulted in a form of balkanisation that tends to cause additional stress to the legacy electricity grid, which has a structure based on centralised command and management of large scale generating plant, long-range high voltage transmission and local low voltage distribution networks. This structure implies expensive standby capacity to meet peak loads; high capital cost and long lead-times for new plant; economic inefficiency due to deadweight losses, external costs and imperfect ‘top-down’ regulation; vulnerability to energy security threats of various kinds; and rigidity to beneficial change such as the increased exploitation of distributed energy resources (DERs) and the development of more flexible and sophisticated energy services that might lead to greater energy efficiency.
Recognition of the Smart Grid as an example of a CAS evolving in the technosphere represents a great opportunity to gain important insights into the emergence of self-organisation and how such systems evolve, in scenarios with extremely high relevance for a range of vital policy issues affecting energy security and carbon reduction
Duration of the meeting 1 Day, 5th September, 2012
- Presentation of CASCADE modelling, Multi-Agent, Spatial modelling of Energy supply and Demand
- Presentation from EIFER the Karlesruhe Institute of Technology
- Invited contribution from the Satellite meeting last year
Call for Papers:
The track will accept a maximum of 6 papers for presentation. Papers should be submitted by 1st June. They will be evaluated and selected by the Workshop Organising Committee with blind refereeing. Submissions should be sent to: https://www.easychair.org/conferences/?conf=eccs12satellites
It is intended that, subject to the usual refereeing procedures, papers from this workshop will be published in a special issue of the journal E:CO
Submission timetable:
6th February – Call for papers
1st June Deadline for Paper Submissions
1st July – notification of paper acceptance to deliver presentation at the workshop
Preliminary agenda
The papers will address the following topics:
Infrastructural networks structure and evolution.
Operational behaviour and catastrophic dynamics of infrastructural networks.
Multi-scaling and hierarchical characterization.
Meta-complexities and infrastructural networks interdependencies.
Biology-inspired resilient networks and metaphors.
Aggregate and agent-based modelling of impacts and dynamic processes in infrastructural networks.