This paper describes how Distributed Ledger Technologies can be used to enforce social contracts and to orchestrate the behaviour of agents trying to access a shared resource. The first part of the paper analyses the advantages and disadvantages of using Distributed Ledger Technologies architectures to implement certain control systems in an Internet of Things (IoT) setting, and then focuses on a specific type of DLT based on a Directed Acyclic Graph. In this setting we propose a set of delay differential equations to describe the dynamical behaviour of the Tangle, an IoTinspired Directed Acyclic Graph designed for the cryptocurrency IOTA. The second part proposes an application of Distributed Ledger Technologies as a mechanism for dynamic deposit pricing, wherein the deposit of digital currency is used to orchestrate access to a network of shared resources. The pricing signal is used as a mechanism to enforce the desired level of compliance according to a predetermined set of rules. After presenting an illustrative example, we analyze the control system and provide sufficient conditions for the stability of the network. I. INTRODUCTORY REMARKSBitcoin, and the technology that underpins it, Blockchain, have recently become a source of great debate and controversy in both business and scientific communities. To its supporters, Distributed Ledger Technology (DLT) (the agnostic term for Blockchain and related technologies)[1][2] is a key technology that will unlock new disruptive business models such as peer to peer trading, protect the rights of individuals, democratise society, and remove the need for central arbiters in many applications (the greedy middle that manages and exploits our assets and identities for financial reward). To its detractors, DLT is nothing more than pure hype, irrational speculation, and a means to enable new forms of illegality built on the anonymity that underpins the technology. DLT as a technology is truly unparalleled in its ability to split and divide opinions. Countries such as Switzerland and Singapore are openly embracing its potential, while other countries, such as China and India are trying to regulate its use [3]. Leading societal thinkers are also split on DLT; with George Soros 1 thinking it to be nothing more than a bubble and others such as Al Gore embracing the idea that algorithms might one day assume some of the functions of government 2 . A first version of this manuscript is posted to the Arxiv, arXiv:1807.00649 1 https://www.forbes.com/sites/gurufocus/2018/01/25/george-soros-fromdavos-bitcoin-is-a-typical-bubble/#7a1d097929d0 2 https://www.forbes.com/sites/ksamani/2017/10/04/how-crypto-willreshape-capitalism-as-we-know-it/2/#5b9daa0e6ed4 Though the schism in DLT thinking is very real, everyone seems to agree on one basic fact -that DLT is potentially a very disruptive technology. Even opponents of the technology are exploring the many ways it can be used, and the consequent potential implications for society. Roughly speaking, as the name suggests, DLT is a technology...

COVID-19 abatement strategies have risks and uncertainties which could lead to repeating waves of infection. We show—as proof of concept grounded on rigorous mathematical evidence—that periodic, high-frequency alternation of into, and out-of, lockdown effectively mitigates second-wave effects, while allowing continued, albeit reduced, economic activity. Periodicity confers (i) predictability, which is essential for economic sustainability, and (ii) robustness, since lockdown periods are not activated by uncertain measurements over short time scales. In turn—while not eliminating the virus—this fast switching policy is sustainable over time, and it mitigates the infection until a vaccine or treatment becomes available, while alleviating the social costs associated with long lockdowns. Typically, the policy might be in the form of 1-day of work followed by 6-days of lockdown every week (or perhaps 2 days working, 5 days off) and it can be modified at a slow-rate based on measurements filtered over longer time scales. Our results highlight the potential efficacy of high frequency switching interventions in post lockdown mitigation. All code is available on Github at https://github.com/V4p1d/FPSP_Covid19. A software tool has also been developed so that interested parties can explore the proof-of-concept system.

The paper proposes a stochastic model to analyse the dynamic coupling of the transmission system, the electricity market and microgrids. The focus is on the impact of microgrids on the transient response of the system and, in particular, on frequency variations. Extensive Monte Carlo simulations are performed on the IEEE 39-bus system, and show that the dynamic response of the transmission system is affected in a non trivial way by both the number and the size of the microgrids

Testing, tracking and tracing abilities have been identified as pivotal in helping countries to safely reopen activities after the first wave of the COVID-19 virus. Contact tracing apps give the unprecedented possibility to reconstruct graphs of daily contacts, so the question is: who should be tested? As human contact networks are known to exhibit community structure, in this paper we show that the Kemeny constant of a graph can be used to identify and analyze bridges between communities in a graph. Our ‘Kemeny indicator’ is the value of the Kemeny constant in the new graph that is obtained when a node is removed from the original graph. We show that testing individuals who are associated with large values of the Kemeny indicator can help in efficiently intercepting new virus outbreaks, when they are still in their early stage. Extensive simulations provide promising results in early identification and in blocking the possible ‘super-spreaders’ links that transmit disease between different communities.

Abstract-This paper proposes a stochastic control strategy, namely the unsynchronized Addictive Increase Multiplicative Decrease (AIMD) algorithm, to manage the power flow of interconnected microgrids (MGs). The proposed control aims at achieving a trade-off between the individual utility function of each MG while ensuring the stability of the grid. Both centralized and decentralized AIMD approaches are considered and compared. Extensive Monte Carlo simulations are performed on the IEEE 39-bus system, and show that the proposed control strategy is able to provide the sought trade-off.

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This paper illustrates and compares the ability of several clustering algorithms to correctly associate a given aggregate daily electrical load curve with its corresponding day of the week. In particular, popular clustering algorithms like the Fuzzy c-Means, Spectral Clustering and Expectation Maximization are compared, and it is shown that the best results are obtained if the daily data are compressed with respect to a single feature, namely the so-called “Morning Slope”. Such a feature-based clustering appears to outperform the clustering results obtained upon using other classic features, and also with respect to using other conventional compression methods, such as the Principal Component Analysis, in all the examined European countries. This result is particularly interesting, as this feature provides a direct physical interpretation that can be used to obtain insights on the structure of the daily load profiles

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