Emerging smart grids have promising potentials to make energy management more efficient than currently possible in today's power grids. Integration of small scale renewables, distributed charging of electrical vehicles and virtual power stations are some of the technological innovations made possible by smart grids. Besides these technological aspects, smart grids also have a clear social component: consumers and small producers can together form energy communities. Such communities can be based on shared geographical location. They can also form based on shared values. This paper assumes that online social networks can be used to form virtual energy communities with shared values such as sustainability and social cohesion, sharing energy. We present an exploratory study on the creation and evolution of Smart Grid Social Networks using an agent-based simulation model. Initial simulation experiments show that in this context a large community with members that are occasionally active forms a better predictor for successful energy communities than a smaller community of very active users.
In traditional engineering, technologies are viewed as the core of the engineering design, in a physical world with a large number of diverse technological artefacts. The real world, however, also includes a huge number of social components -people, communities, institutions, regulations and everything that exists in the human mind -that have shaped and been shaped by the technological components. Smart urban ecosystems are examples of large-scale Socio-Technical Systems (STS) that rely on technologies, in particular on the Internet-of-Things (IoT), within a complex social context where the technologies are embedded. Designing applications that embed both social complexity and IoT in large-scale STS requires a Socio-Technical (ST) approach, which has not yet entered the mainstream of design practice. This chapter reviews the literature and presents our experience of adopting an ST approach to the design of a community-oriented smart grid application. It discusses the challenges, process and outcomes of this apporach, and provides a set of lessons learned derived from this experience that are also deemed relevant to the design of other smart urban ecosystems.
The graph transformation based method presented in this paper can automatically generate simulation models assuming that the models are intended for a certain domain. The method differs from other methods in that: the data used for model generation does not contain specifications of the model structures to be generated; the generated simulation models have structures that are dynamically constructed during the model generation process. Existing data typically has quality issues and does not contain all types of information, particularly in terms of model structure, that are required for modelling. To solve the problem, transformation rules are designed to infer the required model selection and structure information from the data. The rules are specified on meta-models of the original data structure, of intermediate structures and of the simulation model. Graph patterns, pattern composites and graph pattern matching algorithms are used to define and identify potential model components. Model composite structures are represented by hypergraphs according to which simulation models are generated using model components as building blocks. The method has been applied practically in the domain of light-rail transport.
The attitude-behaviour gap in energy consumption refers to the imparity between people's environmental values (and attitudes) and their actual behavior in consumption. This paper calls for the facilitation of the behaviour change process that is implementable in the context of one's everyday life to address the attitude-behaviour gap in household energy consumption. Two interrelated intervention design constructs are proposed based on the results of literature review, namely (1) providing consumers accurate information about actionable suggestions in the specific context of their everyday life, and (2) fostering consumers' motivation to engage in the behavior change process towards energy conservation.
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