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The emergence of peer-to-peer, collective or community self-consumption, and transactive energy concepts gives rise to new configurations of business models for local energy trading among a variety of actors. Much attention has been paid in the academic literature to the transition of the underlying energy system with its macroeconomic market framework. However, fewer contributions focus on the microeconomic aspects of the broad set of involved actors. Even though specific case studies highlight single business models, a comprehensive analysis of emerging business models for the entire set of actors is missing. Following this research gap, this paper conducts a systematic literature review of 135 peer-reviewed journal articles to examine business models of actors operating in local energy markets. From 221 businesses in the reviewed literature, nine macro-actor categories are identified. For each type of market actor, a business model archetype is determined and characterised using the business model canvas. The key elements of each business model archetype are discussed, and areas are highlighted where further research is needed. Finally, this paper outlines the differences of business models for their presence in the three local energy market models. Focusing on the identified customers and partner relationships, this study highlights the key actors per market model and the character of the interactions between market participants.

Peer-to-peer, community or collective self-consumption, and transactive energy markets offer new models for trading energy locally. Over the past five years, there has been significant growth in the amount of academic literature examining how these local energy markets might function. This systematic literature review of 139 peer-reviewed journal articles examines the market designs used in these energy trading models. A modified version of the Business Ecosystem Architecture Modelling framework is used to extract market model information from the literature, and to identify differences and similarities between the models. This paper examines how peer-to-peer, community self-consumption and transactive energy markets are described in current literature. It explores the similarities and differences between these markets in terms of participation, governance structure, topology, and design. This paper systematises peer-to-peer, community self-consumption and transactive energy market designs, identifying six archetypes. Finally, it identifies five evidence gaps which require future research before these markets could be widely adopted. These evidence gaps are the lack of: consideration of physical constraints; a holistic approach to market design and operation; consideration about how these market designs will scale; consideration of information security; and, consideration of market participant privacy.

In recent years, numerous studies have explored the opportunities and challenges for emerging decentralized energy systems and business models. However, few studies have focussed specifically on the economic and social value associated with three emerging models: peer-to-peer energy trading (P2P), community self-consumption (CSC) and transactive energy (TE). This article presents the findings of a systematic literature review to address this gap. The paper makes two main contributions to the literature. Firstly, it offers a synthesis of research on the social and economic value of P2P, CSC and TE systems, concluding that there is evidence for a variety of sources of social value (including energy independence, local benefits, social relationships, environmental responsibility and participation and purpose) and economic value (including via self-consumption of renewable electricity, reduced electricity import costs, and improved electricity export prices). Secondly, it identifies factors and conditions necessary for the success of these models, which include willingness to participate, participant engagement with technology, and project engagement of households and communities, among other factors. Finally, it discusses conflicts and trade-offs in the value propositions of the models, how the three models differ from one another in terms of the value they aim to deliver and some of the open challenges that require further attention by researchers and practitioners.

The idea that there are important parallels between safety and sustainability and that software engineers might be able to take lessons learned from safety and apply them to sustainability has been voiced and initially explored before. This paper extends the analysis of similarities, differences, and potential synergies between the two concepts, according to four different dimensions of these domains: systemicity, complexity, certification and social perception. Copyright © 2015 for this paper by its authors. Copying permitted for private and academic purposes.

In this paper, we discuss the limitations of the current syntactic composition mechanisms in aspect-oriented requirements engineering (AORE). We highlight that such composition mechanisms not only increase coupling between aspects and base concerns but are also insufficient to capture the intentionality of the aspect composition. Furthermore, they force the requirements engineer to reason about semantic influences and trade-offs among aspects from a syntactic perspective. We present a requirements description language (RDL) that enriches the existing natural language requirements specification with semantic information derived from the semantics of the natural language itself. Composition specifications are written based on these semantics rather than requirements syntax hence providing improved means for expressing the intentionality of the composition, in turn facilitating semantics-based reasoning about aspect influences and trade-offs. We also discuss the practicality of the use of this RDL by outlining the automation support for requirements annotation (realized as an extension of the Wmatrix natural language processing tool suite) to expose the semantics which are in turn utilized to facilitate composition and analysis (supported by the MRAT tool).

Aspect-oriented requirements engineering (AORE) provides separation of concerns at the requirements level. In order to cope with concern identification and structuring into different requirements models, tool support is vital to effectively reduce the burden of performing various AORE tasks. This paper describes how the EA-Miner tool-based approach provides automated support for mining various types of concerns from a variety of early stage requirements documents and how these concepts can be structured into specific aspect-oriented requirements models (e.g., viewpoints-based, use-case-based). The key insight for early-stage requirements automation is the use of natural language processing to reason about properties of the requirements as well as the utilization of semantics revealed by the natural language analysis in building the models. Evaluation of EA-Miner shows promising results concerning time-effectiveness and accuracy of undertaking AORE activities and building requirements models. Moreover, an industrial case study conducted at Siemens AG investigated how the tool performs in a real-world setting by analysing what benefits it brings and challenges it faces during AORE analysis. The EA-Miner analysis enabled to find concerns that were considered relevant by a research team at Siemens that is re-implementing the investigated system with aspect-oriented languages. Moreover, the exposure of the tool to industrial requirements written by different developers also revealed some challenges imposed by the structure of the documentation and the different use of vocabulary terms hence providing new paths to explore and improve the tool in the future such as better pre-processing support, “domain synonym” identification and detection of poorly written requirements.