J Richard Snape

ABSTRACT The Complex Adaptive Systems, Cognitive Agents and Distributed Energy (CASCADE) project is developing a framework based on Agent Based Modelling (ABM). The CASCADE Framework can be used both to gain policy and industry relevant insights into the smart grid concept itself and as a platform to design and test distributed ICT solutions for smart grid based business entities. ABM is used to capture the behaviors of different social, economic and technical actors, which may be defined at various levels of abstraction. It is applied to understanding their interactions and can be adapted to include learning processes and emergent patterns. CASCADE models ‘prosumer’ agents (i.e., producers and/or consumers of energy) and ‘aggregator’ agents (e.g., traders of energy in both wholesale and retail markets) at various scales, from large generators and Energy Service Companies down to individual people and devices. The CASCADE Framework is formed of three main subdivisions that link models of electricity supply and demand, the electricity market and power fl ow. It can also model the variability of renewable energy generation caused by the weather, which is an important issue for grid balancing and the profitability of energy suppliers. The development of CASCADE has already yielded some interesting early findings, demonstrating that it is possible for a mediating agent (aggregator) to achieve stable demand flattening across groups of domestic households fitted with smart energy control and communication devices, where direct wholesale price signals had previously been found to produce characteristic complex system instability. In another example, it has demonstrated how large changes in supply mix can be caused even by small changes in demand profile. Ongoing and planned refinements to the Framework will support investigation of demand response at various scales, the integration of the power sector with transport and heat sectors, novel technology adoption and diffusion work, evolution of new smart grid business models, and complex power grid engineering and market interactions.

Energy consumption is notoriously invisible to building users. Communicating energy performance to users presents a significant opportunity to support behaviour change. Access to near real-time consumption data makes ubiquitous energy performance feedback systems a realistic possibility. Non-domestic building energy performance is a complicated issue, so providing simple, intelligible feedback can be difficult. Communicating what building users are supposed to do with the information is still more so. A true closed-loop feedback system must include both communication of information to users and a means for users to affect the building to which the information pertains. This paper reports the design and use of a novel information system to facilitate a true feedback loop between a community of building stakeholders (users, energy professionals, researchers) and 25 pilot buildings. The buildings were equipped to communicate energy performance in near real time via a user-friendly 'dashboard' built on a sophisticated system of automated data capture, energy consumption modelling, predictive statistical analysis and visualisation. The 'dashboard' allowed casual users to access information easily via a simple happy/sad performance indicator whilst more " data-philic " users were able to click through to a data rich, easy-to-use interface. Users were also provided with access to a digital social platform enabling transparent discussion of energy performance with reference to the objective data. Results show that the 'dashboard' and digital social platform components are each valuable in their own right but in combination they produced a system whereby users could identify and solve energy and water performance problems effectively and efficiently.

Smart grid research has tended to be compart-mentalised, with notable contributions from economics, electrical engineering and science and technology studies. However, there is an acknowledged and growing need for an integrated systems approach to the evaluation of smart grid initiatives. The capacity to simulate and explore smart grid possibilities on various scales is key to such an integrated approach but existing models – even if multidisciplinary – tend to have a limited focus. This paper describes an innovative and flexible framework that has been developed to facilitate the simulation of various smart grid scenarios and the interconnected social, technical and economic networks from a complex systems perspective. The architecture is described and related to realised examples of its use, both to model the electricity system as it is today and to model futures that have been envisioned in the literature. Potential future applications of the framework are explored , along with its utility as an analytic and decision support tool for smart grid stakeholders.

ABSTRACT Rising demand from electrical heating and vehicles will drive major distribution network reinforcement costs unless 24-hour demand profiles can be levelled. We propose a demand response scheme in which the electricity supplier provides a signal to a “smart home” control unit that manages the consumer's appliances using a novel approach for reconciliation of the consumer's needs and desires with the incentives supplied by the signal. The control unit allocates demand randomly in timeslots that are acceptable to the consumer but with a probability biased in accordance with the signal provided by the supplier. This behaviour ensures that demand response is predictable and stable and allows demand to be shaped in a way that can satisfy distribution network constraints.

ABSTRACT The ability to influence electricity demand from domestic and small business consumers, so that it can be matched to intermittent renewable generation and distribution network constraints is a key capability of a smart grid. This involves signalling to consumers to indicate when electricity use is desirable or undesirable. However, simply signalling a time-dependent price does not always achieve the required demand response and can result in unstable system behaviour. The authors propose a demand response scheme, in which an aggregator mediates between the consumer and the market and provides a signal to a `smart home' control unit that manages the consumer's appliances, using a novel method for reconciliation of the consumer's needs and preferences with the incentives supplied by the signal. This method involves random allocation of demand within timeslots acceptable to the consumer with a bias depending on the signal provided. By simulating a population of domestic consumers using heat pumps and electric vehicles with properties consistent with UK national statistics, the authors show the method allows total demand to be predicted and shaped in a way that can simultaneously match renewable generation and satisfy network constraints, leading to benefits from reduced use of peaking plant and avoided network reinforcement.

Smart grid research has tended to be compart-mentalised, with notable contributions from economics, electrical engineering and science and technology studies. However, there is an acknowledged and growing need for an integrated systems approach to the evaluation of smart grid initiatives. The capacity to simulate and explore smart grid possibilities on various scales is key to such an integrated approach but existing models – even if multidisciplinary – tend to have a limited focus. This paper describes an innovative and flexible framework that has been developed to facilitate the simulation of various smart grid scenarios and the interconnected social, technical and economic networks from a complex systems perspective. The architecture is described and related to realised examples of its use, both to model the electricity system as it is today and to model futures that have been envisioned in the literature. Potential future applications of the framework are explored , along with its utility as an analytic and decision support tool for smart grid stakeholders.

Investigating the dynamics of consumption is crucial for understanding the wider socio-technical transitions needed to achieve carbon reduction goals in the energy sector.  Such insight is particularly necessary when considering Smart Grids and current debates about potential transition pathways (and contingent benefits) for the electricity system and coupled gas and transport systems. 
The electricity grid is a complex adaptive system comprising physical networks, economic markets and multiple, heterogeneous, interacting agents.  Fundamental to innovation studies is that social practices and technological artefacts shape and are shaped by one another.  Different trajectories of socio-technical systems’ transition are intrinsically linked to the behavioural and cognitive norms of individuals, businesses, communities, sectors, and governance institutions.  Therefore the transition to smart(er) grids inevitably requires a knowledge transition and behaviour change among such actor groups.  To date, these effects have not been modelled.
We present a prototype Agent Based Model (ABM) as a means to examine the effect of individual behaviour and social learning on energy use patterns, from the perspectives of adoption of energy saving behaviours, energy saving technologies and individual or community based energy use practices.  We draw on the Energy Cultures framework to understand real-world observations and incorporate representative energy use behaviours into the model and discuss the model’s relation to case studies, e.g. energy use in island communities. 
Such models enable examination of how far we can learn and scale up lessons from case studies to similar Socio-Technical Systems with bigger scale and greater interconnectivity such as the UK national grid.