A Block-Free Distributed Ledger for P2P Energy Trading:Case with IOTA?

& #x00A9; 2019, Springer Nature Switzerland AG. Across the world, the organisation and operation of the electricity markets is quickly changing, moving towards decentralised, distributed, renewables-based generation with real-time data exchange-based solutions. In order to support this change, blockchain-based distributed ledgers have been proposed for implementation of peer-to-peer energy trading platform. However, blockchain solutions suffer from scalability problems as well as from delays in transaction confirmation. This paper explores the feasibility of using IOTA’s DAG-based block-free distributed ledger for implementation of energy trading platforms. Our agent-based simulation research demonstrates that an IOTA-like DAG-based solution could overcome the constraints that blockchains face in the energy market. However, to be usable for peer-to-peer energy trading, even DAG-based platforms need to consider specificities of energy trading markets (such as structured trading periods and assured confirmation of transactions for every completed period)

A model based approach for complex dynamic decision-making

Current state-of-the-practice and state-of-the-art of decision-making aids are inadequate for modern organisations that deal with significant uncertainty and business dynamism. This paper highlights the limitations of prevalent decision-making aids and proposes a model-based approach that advances the modelling abstraction and analysis machinery for complex dynamic decision-making. In particular, this paper proposes a meta-model to comprehensively represent organisation, establishes the relevance of model-based simulation technique as analysis means, introduces the advancements over actor technology to address analysis needs, and proposes a method to utilise proposed modelling abstraction, analysis technique, and analysis machinery in an effective and convenient manner. The proposed approach is illustrated using a near real-life case-study from a business process outsourcing organisation

OrgML - a domain specific language for organisational decision-making

Effective decision-making based on precise understanding of an organisation is critical for modern organisations to stay competitive in a dynamic and uncertain business environment. However, the state-of-the-art technologies that are relevant in this context are not adequate to capture and quantitatively analyse complex organisations. This paper discerns the necessary information for an organisational decision-making from management viewpoint, discusses inadequacy of the existing enterprise modelling and specification techniques, proposes a domain specific language to capture the necessary information in machine processable form, and demonstrates how the collected information can be used for a simulation-based evidence-driven organisational decision-making

Juxtaposition of system dynamics and agent-based simulation for a case study in immunosenescence

Advances in healthcare and in the quality of life significantly increase human life expectancy. With the aging of populations, new un-faced challenges are brought to science. The human body is naturally selected to be well-functioning until the age of reproduction to keep the species alive. However, as the lifespan extends, unseen problems due to the body deterioration emerge. There are several age-related diseases with no appropriate treatment; therefore, the complex aging phenomena needs further understanding. It is known that immunosenescence is highly correlated to the negative effects of aging. In this work we advocate the use of simulation as a tool to assist the understanding of immune aging phenomena. In particular, we are comparing system dynamics modelling and simulation (SDMS) and agent-based modelling and simulation (ABMS) for the case of age-related depletion of naive T cells in the organism. We address the following research questions: Which simulation approach is more suitable for this problem? Can these approaches be employed interchangeably? Is there any benefit of using one approach compared to the other? Results show that both simulation outcomes closely fit the observed data and existing mathematical model; and the likely contribution of each of the naive T cell repertoire maintenance method can therefore be estimated. The differences observed in the outcomes of both approaches are due to the probabilistic character of ABMS contrasted to SDMS. However, they do not interfere in the overall expected dynamics of the populations. In this case, therefore, they can be employed interchangeably, with SDMS being simpler to implement and taking less computational resources

High Speed Simulation Analytics

Simulation, especially Discrete-event simulation (DES) and Agent-based simulation (ABS), is widely used in industry to support decision making. It is used to create predictive models or Digital Twins of systems used to analyse what-if scenarios, perform sensitivity analytics on data and decisions and even to optimise the impact of decisions. Simulation-based Analytics, or just Simulation Analytics, therefore has a major role to play in Industry 4.0. However, a major issue in Simulation Analytics is speed. Extensive, continuous experimentation demanded by Industry 4.0 can take a significant time, especially if many replications are required. This is compounded by detailed models as these can take a long time to simulate. Distributed Simulation (DS) techniques use multiple computers to either speed up the simulation of a single model by splitting it across the computers and/or to speed up experimentation by running experiments across multiple computers in parallel. This chapter discusses how DS and Simulation Analytics, as well as concepts from contemporary e-Science, can be combined to contribute to the speed problem by creating a new approach called High Speed Simulation Analytics. We present a vision of High Speed Simulation Analytics to show how this might be integrated with the future of Industry 4.0