Accelerated local deployments of renewable energy sources and energy storage units, as well as increased overall flexibility in local demand and supply through active user involvement and smart energy solutions, open up new opportunities (e.g., self-sufficiency and CO 2 neutrality through local renewables) and yet pose new challenges (e.g., how to maintain the security of supply and get the best yield) to market players in the lower parts of the energy system (including prosumers, energy communities, aggregators, and distribution system operators (DSOs)). One way to cope with the challenges requires "logical" reorganization of the energy system bottom-up as a number of nested (maximally) self-sufficient and interacting cells with their own local (i.e. within a cell) energy management and trading capabilities. This change necessitates effective IT-based solutions. Towards this goal, we propose a unified Flexibility Modeling, Management, and Trading System (FMTS) that generalizes flexibility modeling, management, and intra-cell trading in such cellular energy systems. Our system offers different flexibility provisioning options (Machine Learning based, and Model Predictive Control based), activation mechanisms (indirect and direct device-control), and trading schemes (e.g. flexibility contracts, market-based trading) and suits different cellular system use-cases. In this paper, we introduce the FMTS, overview its core functionality and components, and explain how it practically manages, prices, and trades flexibility from a diverse variety of loads. We then introduce the real-world FMTS instances developed in the GOFLEX project 1 and present experimental results that demonstrate significantly increased flexibility capacities, user gains, and balance between demand and supply when an FMTS instance is used in the simulated cellular energy system setting.

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The recent spread of distributed renewable energy sources and smart IoT devices offer exciting new possibilities for the use of energy flexibility, opening a new era of the socalled bottom-up or cellular energy systems. In order to harness the full potential of flexibility, flexibility has to be modeled and represented in a manner that can be efficiently managed, manipulated, and traded on a market. In this paper, we provide a comprehensive overview of the FlexOffer concept, which offers an effective way of modeling and managing energy demand and supply flexibilities from a wide range of flexible resources and their aggregates. First, we define the basic concept and present the different phases of the FlexOffer life-cycle. Then, we discuss more advanced internal FlexOffer constraints as well as algorithms for FlexOffer generation, aggregation, disaggregation, and pricing that can significantly reduce energy management and trading complexities and increase overall efficiency. Finally, we present a general decentralized system architecture for trading flexibility (FlexOffers) in existing and new markets. Our experimental results show that (1) FlexOffers can be extracted with up to 98% accuracy, (2) aggregation and disaggregation can scale to 1000K FlexOffers and more, and (3) flexibility can be traded in the NordPool flexi order market while providing up to 89.9% (of optimal) reduction in the energy cost.

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In this paper, we perform an econometric analysis on the benefits of introducing flexibility in the Danish/Nordic regulating power market. The paper investigates the relationships between market power prices and regulation volumes, in order to quantify the effects of flexibility on regulating power prices. Further, we analyze the benefit for various types of flexibility and market objectives, to detect the type of energy flexibility that maximizes the benefits. Results show that if 3.87% of total demand is flexible, the market can reduce the regulation cost by 49% and the regulation volume by 29.4%.

Abstract. By diverting funds away from legitimate partners (a.k.a publishers), click fraud represents a serious drain on advertising budgets and can seriously harm the viability of the internet advertising market. As such, fraud detection algorithms which can identify fraudulent behavior based on user click patterns are extremely valuable. Based on the BuzzCity dataset, we propose a novel approach for click fraud detection which is based on a set of new features derived from existing attributes. The proposed model is evaluated in terms of the resulting precision, recall and the area under the ROC curve. A final ensemble model based on 6 different learning algorithms proved to be stable with respect to all 3 performance indicators. Our final model shows improved results on training, validation and test datasets, thus demonstrating its generalizability to different datasets.

e uncertainty in the power supply due to uctuating Renewable Energy Sources (RES) has severe ( nancial and other) implications for energy market players. In this paper, we present a device-level Demand Response (DR) scheme that captures the atomic (all available) exibilities in energy demand and provides the largest possible solution space to generate demand/supply schedules that minimize market imbalances. We evaluate the e ectiveness and feasibility of widely used forecasting models for device-level exibility analysis. In a typical device-level exibility forecast, a market player is more concerned with the utility that the demand exibility brings to the market, rather than the intrinsic forecast accuracy. In this regard, we provide comprehensive predictive modeling and scheduling of demand exibility from household appliances to demonstrate the ( nancial and otherwise) viability of introducing exibility-based DR in the Danish/Nordic market. Further, we investigate the correlation between the potential utility and the accuracy of the demand forecast model. Furthermore, we perform a number of experiments to determine the data granularity that provides the best nancial reward to market players for adopting the proposed DR scheme. A cost-bene t analysis of forecast results shows that even with somewhat low forecast accuracy, market players can achieve regulation cost savings of 54% of the theoretically optimal.