This Special Issue arises mainly out of a Satellite Workshop of the European Conference on Complex Systems (ECCS) held in Brussels in September 2012. The organizers of this Workshop were Peter Allen and Liz Varga from Cranfield University, UK (Complex Systems Research Centre), Mark Rylatt, Rupert Gammon and Peter Boait from De Montfort University, UK (Institute of Energy and Sustainable Development) who are all members of the CASCADE Project. This satellite was also organized with Enrique Kremers from EIFER (European Institute for Energy Re-search – Karlsruhe Institute of Technology)
The overall rationale of the Workshop and of this Special Issue concerns the need for low carbon electricity generation in the future. The problem posed by the probable link between carbon emissions and climate change means that there is a vital need to change energy production from their current fossil fuel basis towards, renewables such as solar, wind and biomass and other less carbon intensive sources and means of generation.
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; 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.
This leads on to questions of how to deal with the increased intermittency of supply that results when we move away from dispatchable energy generation. Moving away from the traditional model of centralized generation, by coal, gas or oil based power stations, implies adjustments to the electricity system to successfully deal with the distributed, more intermittent generation and possibly growing demand., including restructuring of the wholesale and retail energy markets and engagement with advances promised by the concept of the Smart Grid. What is of interest is the role of Complexity Science in allowing us to make progress in providing knowledge and support for the radical infrastructure changes that seem necessary between now and 2050 if the catastrophe of major climate change is to be averted.
The level of carbon emissions that we allow to continue is linked to the extent of future Climate Change that we are likely to face and to the possibility of massive disruption and disaster if we do not succeed in reducing 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 will be required. In the UK the ambitious 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. The system is likely to become less ‘top-down’ and centralised in its organization and control towards something more distributed and self-organizing, which will have characteristics of a Complex Adaptive System. The possible consequences of this are being studied using complexity science.
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 standby capacity needed to deal with peak loads and with the new problem of intermittent large scale renewable generation. This is of great interest from a Complexity Science standpoint, offering opportunities to address a real world problem by envisioning more clearly possible future paths for the development of the grid —, itself a system of systems at a task oriented level of description, within a broader complex system of systems including economic, technological, social weather and climate,, for example radically away from the centralised structure of national power systems evident today. The uncertainties inherent in the conception of the Smart Grid are now of international interest and concern. There is a need to raise the 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. At another level of description, the Smart Grid has been conceptualized potentially as a Complex Adaptive System (CAS), for example in studies such as CASCADE, which aims to provide a framework for the investigation of these phenomena and their implications across technical, socio-technical and socioeconomic domains as the CAS evolve over time.
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-organization and how such systems evolve, in scenarios with extremely high relevance for a range of vital policy issues affecting energy security and carbon reduction.
At a model implementation level one interesting question is whether we can develop models that will represent the learning and behavior change of actors involved in supply and demand with regard to the different technologies of electricity generation as well as the Smart Grid. At the system level another important question is whether a dynamic, distributed multi-agent system for power generation and distribution might be inherently unstable as agents try to ‘game’ it, and causing catastrophic crashes reminiscent of those experienced by financial systems. Centralized, top-down systems were boring perhaps, but not subject to problems of this kind. Studies and models like CASCADE will increasingly be needed to explore what policies and governance frameworks will be necessary for future sustainable energy systems.
Given current and likely technologies by 2050, sustainable, low carbon energy production and distribution require spatially distributed, intermittent resources and that energy demand and supply interact directly (successfully) at many levels through sensors and effectors and indirectly through technical and human-machine interfaces and through the operation of market systems. How much more complex should the market systems of this new grid be from those of our current system (e.g., with consumer-producer agents selecting a portfolio of technologies with varying characteristics in addition to any purchases from other producers, so as to balance cost, risk, and other considerations)? Complexity studies tell us that simple, understandable, approximate representations might fail to reveal the emergent properties that may occur in the real world system, as by their nature these global phenomena arise from a plethora of local interactions. However, multi-scale problems of the kind to be studied here call for tractable approaches if they are not to be thwarted by complexity of the computational kind. What trade-offs are necessary/acceptable?
The papers presented here address some of these issues and also reveal the limits of our capacity to predict the future. The first paper (Rylatt et al.) is an overview of the model developed within the CASCADE project. It is a multi-agent model of the half-hourly dynamics of future wholesale and retail electricity markets with an underlying engineering model that captures the essential physical characteristics of the grid. The next paper (Kremers et al.) presents a real application for an Island (La Reunion) as part of the project Millener. The next paper (Boait et al.) describes models that help to explore the dynamic control of demand, using a Smart Grid. The next paper looks at the evolution of the UK electricity system and provides a scenario within which the main CASCADE model sits. It models the transformation of UK generation from coal, nuclear and gas over to wind, solar, and nuclear as the 40 years to 2050 are used to transform the generating capacity of the UK. It demonstrates what an enormous task this will be. The final paper looks at how the local demand coming from a small neighborhood could be smoothed successfully by local battery storage, reducing peak demand by 50% and therefore reducing the demand for new capacity.
Clearly, alongside the smart grid, new technologies are emerging and old technologies are being revamped as new ideas are being explored in order to try to deal with this pressing and most important range of problems Some of these may even reduce or remove some of the problems of complexity given the necessary technological advances in for example electricity storage technology. Among them are: new sources of gas from Fracking, new energy storage technologies for electrical, thermal or kinetic batteries, on and off-shore wind farms, new types of (less dangerous) nuclear reactors, wave and tidal energy harvesters, and perhaps clean coal and gas plants where the emitted CO2 is sequestered. All of these, and many more, including popular sentiments, are changing the forms and parameters of possible responses, and we see that the future is not at all given, but will be affected by all kinds of interacting factors and events. This is an important feature of complex systems, whose constituent parts are coevolving and changing potentially qualitatively. There is no given future to predict, as predictions themselves affect what happens, and so what is set out here are contributions to the development of more flexible, adaptive systems on which possible futures and possible choices can be assessed.