By Jonathan Spencer Jones

Complexity science is attracting increasing interest to understand the behavior of complex systems with large numbers of interacting components, and now, according to the intriguingly titled “Smart Energy Grids and Complexity Science” from Europe’s Joint Research Center, it should also be applied to smart grids.

Rooted in subjects such as cybernetics, and chaos and catastrophe theory, complexity science is a broad and multi-disciplinary subject that is finding increasing application in fields as diverse as biology, economics, IT networks and business. But ‘complex’ in this context doesn’t just indicate ‘complicated’ and rather is focused on the global dynamics that result from the multiple interactions in the system – but that cannot easily be explained in terms of these individually.

Applying this to the smart grid system, the complexity arises not from the smart grid itself, undeniably complex though it may be, with multiple layers such as physical, cyber, social, policy, and decision making layers. But these layers also interact with changing external conditions, such as economic cycles, technological innovation, and changing weather and climatic conditions. Further, many actors interact within this broader ‘system of systems’, including prosumers, distributors, retailers, brokers, regulators and policy makers.

Thus, states the report – which is primarily a collection of the presentations from a June 2012 meeting on the topic – the complexity of the smart grid system rests on the multiplicity of interacting players that operate with, and within, a defined environment as independent decision makers, with autonomous behaviors, goals and attitudes. These broader socio-technical networks form a community with high levels of interaction and integration among its actors.

Accordingly, in this context, in order to understand the complexity of future smart grids, there is the need to move focus and attention from a component-oriented to an interaction-oriented view of the electric power system. Modeling and analyzing the dynamics and interactions of the relevant actors and components should help in identifying tools and techniques for optimal decision making encompassing policy and regulatory design, planning and investment, as well as real time operations.

As examples of such modeling in action, one of the presentations considers topological analysis of the network. Another proposes modeling the network as an open thermodynamic system using statistical thermodynamics, with the power modeled as a plasma and its flow correlated with the system entropy.

Considering the hypothesis that future smart energy system research incorporating complexity sciences can provide models and guidelines for future developments and for recognizing emerging behaviors and challenges, the researchers propose a four part research agenda:

  1. Formalization of a framework, including identification of relevant components, goals and interactions
  2. Definition of a formalized environment for studying future smart energy systems
  3. Development (theoretical and practical implementation), according to the identified evolutionary scenarios, of models on various scales of the various layers and integration for serving general purposes in developing future smart energy systems.
  4. Application to case studies, and verification and validation with model comparison with different real benchmark cases.

Key research questions include:

  • Can complexity sciences help in understanding, modeling and simulating the emerging smart grid environment within a broader sustainability context including changes in economies, consumer and social behavior, and climate variability and adaptation?
  • Can sound policy decision making be based on theoretical models and simulation tools derived in the framework of dynamic multilayer interacting complex systems?
  • How can the multi-layered, multi-actor energy system satisfy economic, environmental, security and social requirements?
  • How can complexity science help in better addressing the threats and risks affecting future energy systems?
  • How can future technological and social changes be anticipated, managed and integrated in policy and decision making?
  • How can complexity science help in addressing these socio-technical challenges?
  • What is the role played by “contextual” complexity due to the social environment, climate scenarios etc., and how can we properly address such complexity?

With complex systems science for smart grids now a research stream at the JRC, this will be a subject to follow with interest.