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As latest advancements signify the fourth industrial revolution, artificial intelligence (AI) and internet of things (IoT) became the focal points for innovators. IoT-enabled technology can be used to gather and explore huge amounts of data from both virtual and physical environments, and AI provides the means for effectively processing and manipulating resulting information to optimize or automate processes. In this chapter, the related state of the art is presented, along with the characteristics that enable the creation of hybrid innovation ecosystems. An overview of IoT and AI platforms is included, which can be utilized even by non-experts to compose advanced cost-effective services. Also, related notions such as interoperability and engagement are also discussed. Although such components can be applied in a multitude of domains, to provide a concrete example of innovation enablement, the smart grid ecosystem is employed. Here, participants, either from the supply or the demand side, take advantage of IoT and AI technology to address new business requirements that arise.

Η εξισορρόπηση της ζήτησης και της παραγωγής ενέργειας στα σύγχρονα έξυπνα δίκτυα με αυξημένη διείσδυση των ανανεώσιμων πηγών ενέργειας είναι ένα μεγάλο πρόβλημα, για τη λύση του οποίου απαιτούνται περίπλοκοι μηχανισμοί για την αποτελεσματική διαχείριση της ζήτησης ενέργειας (DSM). Σε αυτή τη διατριβή, προτείνουμε μηχανισμούς για το σχηματισμό συνεταιρισμών ευφυών πρακτόρων που προσφέρουν υπηρεσίες DSM μεγάλης κλίμακας, και παρουσιάζουμε ένα ολοκληρωμένο πλαίσιο λειτουργίας. Τα σχήματα λαμβάνουν υπόψη τα ατομικά κόστη μετατόπισης κατανάλωσης και ανταμείβουν τους συνεισφέροντες ανάλογα με την αποδοτικότητα τους. Επίσης, για να επιτρέψουμε τον αποκεντρωμένο συντονισμό των συνεταιρισμών καταναλωτών-παραγωγών ενέργειας,συνδυάζουμε έναν αυστηρά αρμόζοντα κανόνα βαθμολόγησης με ένα κρυπτονόμισμα βασισμένο σε “αλυσίδα από μπλοκ” (blockchain). Επιπλέον, προτείνουμε αλγόριθμο για φόρτιση και αποφόρτιση ηλεκτρικών οχημάτων που προωθεί επιχειρηματικά μοντέλα που κάνουν χρήση της δυνατότητας των ηλ. οχημάτων να αποθηκεύουν ενέργεια. Συμπληρωματικά, για να αξιολογήσουμε την αβεβαιότητα των συμμετεχόντων, και να προβλέψουμε σωστά τη μελλοντική τους συμπεριφορά, υιοθετούμε τεχνικές μηχανικής μάθησης. Τέλος, παρέχουμε μεθοδολογία για την παροχή υπηρεσιών διαχείρισης ζήτησης (DSM) μεγάλης κλίμακας βασισμένη σε αρχιτεκτονική διαδικτύου των πραγμάτων (IoT). Τα αποτελέσματα από τις προσομοιώσεις που χρησιμοποιούν δεδομένα από τον πραγματικό κόσμο, δείχνουν ότι η χρήση των προτεινόμενων μεθόδων σε συνθήκες μεγάλης κλίμακας μπορεί να ωφελήσει σημαντικά τους τελικούς χρήστες, το δίκτυο, και το περιβάλλον.

Summarization: A variety of multi-agent systems methods has been proposed for forming cooperatives of interconnected agents representing electricity producers or consumers in the Smart Grid. One major problem that arises in this domain is assessing participating agents' uncertainty, and correctly predicting their future behaviour regarding power consumption shifting actions. In this paper we adopt various machine learning techniques and use these to effectively monitor the trustworthiness of agent statements regarding their final shifting actions. In particular, we evaluate the performance of four approaches, one based on a Histogram Filter, and three on regression approaches, that is, Gaussian Process, k-Nearest Neighbours, and Kernel Regression. We incorporate these to aggregate individual forecasts within a directly applicable scheme for providing cooperative electricity demand shifting services. Experiments were conducted on real-world datasets from thousands of users located in Kissamos, a municipality of Crete. Our results confirm that the adoption of machine learning techniques provides tangible benefits regarding enhanced cooperative performance, and increased financial gains for the participants. Παρουσιάστηκε στο: Advances in Building Energy Research

Summarization: As the penetration of renewables into the grid increases, so do the uncertainty and constraints that need to be taken into account during demand-side management (DSM). Mechanism design (MD) provides effective DSM solutions that incorporate end-user preferences and uncertain capabilities, without jeopardizing comfort. The authors discuss the state of the art in DSM methods, which they broadly classify as game theoretic (largely MD) and not. They then proceed to outline a novel scheme for large-scale coordinated demand shifting, a highly important problem. Their mechanism employs the services of cooperatives of electricity consumers, incentivizes truthfulness regarding the contributions promised by the participants, and incorporates profiling techniques that assess contributors' trustworthiness. It is shown via simulations over real-world datasets to effectively shift peak load and generate substantial economic benefits at cooperative and individual levels. Παρουσιάστηκε στο: IEEE Intelligent Systems

Managing energy consumption and production is a challenging problem and proactive balancing between the amount of electricity produced and consumed is needed. In this work, we examine mechanisms that give incentives to consumers to efficiently reschedule their demand, thus balancing the overall energy production and consumption. Viewing the smart grid as a MAS, each agent represents a consumer; this agent takes into account its user's preferences and proposes an optimal energy consumption plan via a gamified GUI. To implement this we propose a distributed architecture through which we give the incentives (either economic, or social); we test a number of pricing mechanisms and we develop a very fast agent optimization strategy. We also present experiments both from software simulations on real data and pilot tests with human participants: the simulations allow to evaluate the mechanisms and agents, whilst the gamified tests are useful to assess the usability of the GUI and the usefulness of the agent suggestions. With human subjects, we evaluated which type of incentives is more compelling: economic or social. Results validate that by using our agent optimization approach the performance of the smart grid can be improved, and that specific mechanisms allow better utilization of renewable sources.

Summarization: The PowerTAC competition provides a multi-agent simulation platform for electricity markets, in which intelligent agents acting as electricity brokers compete with each other aiming to maximize their profits. Typically, the gains of agents increase as the number of their customers rises, but in parallel, costs also increase as a result of higher transmission fees that need to be paid by the electricity broker. Thus, agents that aim to take over a disproportionately high share of the market, often end up with losses due to being obliged to pay huge transmission capacity fees. In this paper, we present a novel trading strategy that, based on this observation, aims to balance gains against costs; and was utilized by the champion of the PowerTAC-2020 tournament, TUC-TAC. The approach also incorporates a wholesale market strategy that employs Monte Carlo Tree Search to determine TUC-TAC’s best course of action when participating in the market’s double auctions. The strategy is improved by making effective use of a forecasting module that seeks to predict upcoming peaks in demand, since in such intervals incurred costs significantly increase. A post-tournament analysis is also included in this paper, to help draw important lessons regarding the strengths and weaknesses of the various strategies used in the PowerTAC-2020 competition. Παρουσιάστηκε στο: 18th European Conference on Multi-Agent Systems