Bottom-up self-organization of unpredictable demand and supply under decentralized power management

In the DEZENT1 project we had established a distributed base model for negotiating electric power from widely distributed (renewable) power sources on multiple levels in succession. Negotiation strategies would be intelligently adjusted by the agents, through (distributed) Reinforcement Learning procedures. The distribution of the negotiated power quantities (under distributed control as well) occurs such that the grid stability is guaranteed, under 0.5 sec. The major objective in this paper was to deal, on the same level of granularity, with short-term power balance fluctuation, in terms of a peak demand and supply management exhibiting highly dynamic, self-organizing, autonomous yet coordinated algorithms under fine-grained distributed control. Our extensive experiments show very clearly that these short-term fluctuations could be leveled down by 70 - 75 %. In this way we have tackled, for the quickly increasing renewable power systems, a crucial problem of its stability, in a novel way that scales very easily due to the completely decentralized control

Coping with Extreme Events: Institutional Flocking

Recent measurements in the North Atlantic confirm that the thermohaline circulation driving the Gulf Stream has come to a stand. Oceanographic monitoring over the last 50 years already showed that the circulation was weakening. Under the influence of the large inflow of melting water in Northern Atlantic waters during last summer, it has now virtually stopped. Consequently, the KNMI and the RIVM estimate the average . In this essay we will explore how such a new risk profile affects the distribution of risks among societal groups, and the way in which governing institutions need to adapt in order to be prepared for situations of rapid but unknown change. The next section will first introduce an analytical perspective, building upon the Risk Society thesis and a proposed model of ‘institutional flocking’.temperature to decrease by 3°C in the next 15 years

Agent-Based Computational Modeling And Macroeconomics

Agent-based Computational Economics (ACE) is the computational study of economic processes modeled as dynamic systems of interacting agents. This essay discusses the potential use of ACE modeling tools for the study of macroeconomic systems. Points are illustrated using an ACE model of a two-sector decentralized market economy. Related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/amulmark.htmagent-based computational economics

Transaction cost analysis of digital innovation governance in the UK energy market

Energy markets are undergoing significant changes. Legacy systems developed around inflexible, centralized and monodirectional supplies are being replaced by flexible, distributed and bidirectional supply-and-demand systems. Where legacy systems are less entrenched, such as in decentralized renewable energy, flexibility and energy service markets, the pace of change is faster, and new technologies, business models and ideas are more likely to be tested and applied. This conceptual paper analyzes the changing governance of decentralized renewable energy, flexibility and energy services in the United Kingdom from a transaction cost perspective. Particular emphasis is placed on the impact of potentially disruptive innovations such as distributed ledgers, emerging digital technologies and big data analytics on the one hand, and the need for value creation from just and affordable decarbonization on the other. In doing so, this paper sheds light on some contradictions between current energy governance and the requirements for a decarbonized, decentralized, digitalized and democratized energy system. The paper concludes that energy governance is increasingly shaped by decentralization and digitalization, which can either facilitate or inhibit value creation through democratization (social value) and decarbonization (environmental value)

People centred eco-design: consumer adoption of low and zero carbon products and systems

Literature review, research model and findings of exploratory empirical research on consumer adoption and effective use of low and zero carbon technologies ranging from a hybrid car to solar water heating systems

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda