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.
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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