Spatial and Temporal Characteristics of PV Adoption in the UK and Their Implications for the Smart Grid

Published in a full open access journalDistributed renewable electricity generators facilitate decarbonising the electricity network, and the smart grid allows higher renewable penetration while improving efficiency. Smart grid scenarios often emphasise localised control, balancing small renewable generation with consumer electricity demand. This research investigates the applicability of proposed decentralised smart grid scenarios utilising a mixed strategy: quantitative analysis of PV adoption data and qualitative policy analysis focusing on policy design, apparent drivers for adoption of the deviation of observed data from the feed-in tariff impact assessment predictions. Analysis reveals that areas of similar installed PV capacity are clustered, indicating a strong dependence on local conditions for PV adoption. Analysing time series of PV adoption finds that it fits neither neo-classical predictions, nor diffusion of innovation S-curves of adoption cleanly. This suggests the influence of external factors on the decision making process. It is shown that clusters of low installed PV capacity coincide with areas of high population density and vice versa, implying that while visions of locally-balanced smart grids may be viable in certain rural and suburban areas, applicability to urban centres may be limited. Taken in combination, the data analysis, policy impact and socio-psychological drivers of adoption demonstrate the need for a multi-disciplinary approach to understanding and modelling the adoption of technology necessary to enable the future smart grid

Investigating the peculiarities of sustainable energy policies in islands communities for smart grid development: insights from complexity science and agent based models

Initiatives and projects ranging from European islands to the Falklands and the Pacific Fiji islands are implementing renewable energy sources. They not only address the unique features of islands but also to reduce the economic vulnerability of small island states and in some cases, regenerate depopulated island communities and enhance socio-economic and ecological sustainability. Islands are often regarded as laboratories for, or precursors of, wider energy transitions and the “smart grid” innovation makes no exception. The “smart grid” is an umbrella term that covers modernization of both the transmission and active distribution grids and the different competing smart grid architectures could transform the electricity industry and the relations with consumers and prosumers. This paper asks two – relatively simple – questions: are there any socio-technical energy systems and dominant designs more prone to emerge depending on the topologies and scale of islands? How far can we learn and scale up lessons from the studies of island energy communities that are useful in other Complex Adaptive Systems (CAS) with greater scale and interconnectivity? This exploratory paper is part of on-going research project (CASCADE) to model smart grids as Agent Based Systems embracing concepts and techniques from Complexity Science. There are three key objectives. The paper initially summarizes the key particuliarities of island energy systems, including the scale and boundaries to the socio-technical system that combine to determine the appropriateness of different energy responses, balancing and optimizing the various combinations of distributed renewable generation, energy storage (including plug-in cars), and loads. From this, a provisional conceptual model will be presented which identifies the range of factors that (re)configure to influence the potential dissemination of new energy technologies within island communities and the range of agents that influence that process. The paper will build on an expanding literature on modelling societal transitions with cognitive agents and agent transformation to justify our modelling choices. Central to the question is how to represent the cognitive agents and their adoption of new technologies and adaptation patterns.Validation may benefit from data from the Bornholm smartgrid case and other case studies

Smart grids, local adoption of distributed generation and the feed in tariff policy incentive

Smart Grids are often proposed as a means make the most efficient use of available network infrastructure. To deliver such benefits, Smart Grids rely on the adaptation of various consumption practices on the part of the domestic consumer. In addition, the concept requires the adoption of various enabling technologies. The diffusion of these innovations, in both practice and technology, are crucial to the success of the Smart Grid as a means of decarbonisation and efficient infrastructure usage. This paper focuses on the role of domestic users of the electricity network as potential adopters of renewable micro-generation. Data on UK household adoption of micro-generation in the UK in response to national Feed in Tariff policy are analysed from both a temporal and spatial perspective. An Agent Based Model is presented and used to investigate the speed and scale of technology adoption in the presence of policy incentivisation. Heterogeneous agent behaviour is simulated, using parameters from prior research and the data analysis presented to simulate different users’ patterns of consumption and consumers’ adoption strategies, including peer effects. We illustrate the impact that micro-generation adoption, in particular photovoltaic panels, will have on energy consumption, particularly the geographic location of distributed generation as compared to consumption and urban centres. We explore how such adoption may change the typical consumption pattern of both individual households and aggregated groups of households directly and consider research findings on indirect impacts of micro-generation on householder consumption. We discuss the implications of these findings for visions of the electricity network as a Smart Grid and for energy policies designed to promote both adoption of micro-generation and change of consumption behaviour in the Smart Grid context

Incorporating human behaviour in an agent based model of technology adoption in the transition to a smart grid

The requirement for affordable, secure and sustainable energy production is a pressing global challenge and the production of electricity with low carbon emissions is crucial. This usually entails large quantities of renewable energy generation, which is intermittent and often highly distributed throughout the electricity supply system. One of the proposed schemes to manage such generation is the smart grid, the transition to which forms the context for this research. The aim is to investigate the effect of certain psychological and social influences on the adoption of technology necessary to enable smart grids, in order to understand the implications for effective energy policy. In particular, the case of photovoltaic (PV) system adoption in the UK is studied. Empirical data detailing PV installations registered for the Feed in Tariff is analysed in order to understand rates of adoption and how they vary across both time and space. This analysis is combined with a review of policy intervention and literature from psychology to understand drivers for adoption among householders. The results from this study are then used to inform the design of an Agent Based Model of technology adoption within the smart grid context. The decision making of householders is modelled using an algorithm based on Social Cognitive Theory. The model is used to simulate different conditions and generate adoption scenarios in order to understand the potential effects of different parameters on adoption rates. In order to combine the analysis resulting from these methods, the multi-level perspective on transition in socio-technical systems is used to understand how a transition to a smart grid could be described and how adoption of PV in the UK under the Feed in Tariff incentive fits into such a transition. The results show that whilst economic incentive policies have had success in some areas adoption is also dependent on many non-financial parameters. Simulations show that the observability of adoption and the perceived inconvenience or urgency of adoption can have dramatic effects on rates of adoption, in some cases outweighing the rational economic effects of financial incentives. The implication for smart grid related policy is that non-financial factors should be taken into account as well as the more typical financial considerations in efforts to encourage adoption of necessary enabling technology by householders. The models developed could be used in further work to examine in detail adoption of other technologies such as smart home energy management systems and the interaction between adoption rates of multiple smart technologies.The initial 3 years of the PhD were funded by a bursary from the Engineering and Physical Sciences Research Council (EPSRC) under grant number EP/GO59969/1

Understanding energy behaviours and transitions through the lens of a smart grid Agent Based Model

Available from: .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.EPSRC - grant EP/G059969/

Levelling of heating and vehicle demand in distribution networks using randomised device control

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

Making Legacy Thermal Storage Heating fit for the Smart Grid

Collaborative paper with Oxford University Environmental Change Institute and Energy Local Ltd. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Thermal storage heaters, charged using overnight off-peak electricity, have been used for domestic space heating in the UK and other countries since the 1980s. However, they have always been difficult for consumers to manage efficiently and, with the advent of a high proportion of renewables in the electricity generation mix, the time of day when they are charged needs to be more flexible. There is also a need to reduce peaks in the demand profile to allow distribution networks to support new sources of demand such as electric vehicles. We describe a trial of a smart control system that was retrofitted to a group of six dwellings with this form of heating, with the objectives of providing more convenient and efficient control for the users while varying the times at which charging is performed, to flatten the profile of demand and make use of locally-generated renewable electricity. The trial also employs a commercially-realistic combination of a static time-of-day tariff with a real time tariff dependent on local generation, to provide consumers with the opportunity and incentive to reduce their costs by varying times of use of appliances. Results from operation over the 2015-16 heating season indicate that the objectives are largely achieved. It is estimated that on an annualised and weather-adjusted basis most of the users have consumed less electricity than before intervention and their costs are less on the trial tariffs. Critical factors for success of this form of system are identified, particularly the need to facilitate hands-on control of heating by thrifty users and the importance of an effective and sustained user engagement programme when introducing the technology, to ensure users gain confidence through a readily-accessible source of support and advice

Enhancing energy efficiency through smart control: paths and policies for deployment

Smart devices and controllers are often proposed as an effective way to both minimise and optimise the timing of energy consumption in order to minimise the peaks in demand. A key component of the Smart Grid vision is the widespread use of such devices, advanced as a way to mitigate the intermittency of renewable energy generation which in turn is crucial to the decarbonisation of electricity supply. In this paper, we focus on the use of smart controllers and the adoption of distributed renewable generation at household level as part of the transition from a conventional electricity grid to a Smart Grid. We utilise an Agent Based Model to investigate the effectiveness of both smart controllers and distributed generation in reducing household energy consumption, alone and in combination. We also investigate the possible paths to adoption of such devices and the interdependence of the case to adopt one on the other. Electricity consumption patterns for households in the model are heterogeneous and generated in accordance with data for the UK and initial adoption rates for distributed generation are calibrated from UK National data. We illustrate the potential for smart controllers to alter demand patterns over time both with and without distributed generation. We show the effect of order of adoption of devices at the householder level on the energy consumption of their building, but also on consumption at a larger scale and highlight issues for policy makers designing policies intended to incentivise a transition towards smart control of energy demand

Managing complexity in the smart grid through a new approach to demand response

CASCADE was a consortium project with Cranfield UniversityAdoption of weather-dependent renewable generation of electricity has introduced additional complexity to the challenge of maintaining a dynamic equilibrium between generation and electricity demand. At the same time the need for electricity to power heating and transport in place of fossil fuels will lead to congestion in distribution networks. Part of the solution will be to manage domestic electricity demand using signals between the smart grid and smart home, but this must be done in a way that does not provoke further instability. We use an agent-based model of household electricity consumption and supply to show how the complexity of domestic demand can be shaped allowing it to make a contribution to system stability. A possible role for this method in balancing conflicting interests between electricity consumers, suppliers, and distribution network operators is discussedEPSRC under the CASCADE project (EP/GO59969/1

Closing the feedback loop: A systems approach to supporting community-wide behaviour change in non-domestic buildings

The work was partly funded by the SMARTSPACES project (http://smartspaces.eu) co-funded by the European Commission within the CIP ICT Policy Support Programme (Grant agreement no. 297273).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