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a SCAlable & modular system for eNERGY trading between prosumers

Final Report Summary - SCANERGY (a SCAlable & modular system for eNERGY trading between prosumers)

The SCANERGY Project, which started on February 1st 2013, is funded under the 7th Framework Program (Marie Curie Actions) by the European Commission and finished in January 2017. The SCANERGY project is developed by 2 organizations from 2 European Countries: Belgium and Spain.
SCANERGY ( is a vehicle that form and consolidate a team of researchers towards the development of intelligent algorithms for energy management in the context of a smart city. SCANERGY implies several research and training activities with the objective to integrate it in the industrial domain and produce marketable results, in terms of products and services. The SCANERGY project implemented a novel SCAlable & modular system for eNERGY trading between consumers and producers (prosumers). The SCANERGY system combines existing, as well as novel ICT modules allowing for an efficient and distributed energy trading between prosumers. The modular aspect and distributed nature of the system offer scalability and robustness to accommodate the growing need for an intelligent decision support system that manages the renewable energy market.
Within SCANERGY we have developed a scalable and modular system that supports the energy trading between producers and consumers of energy. This system allows individual prosumers to collectively reduce their dependency on the main grid by trading locally produced renewable energy between dwellings. In SCANERGY we have developed methods, implemented as individual modules, to enable the distributed cooperation between energy prosumers with the aim to gain an understanding of how such cooperation can be stimulated between individuals and how to organize this cooperation in the energy sector towards a commercial product.
SCANERGY includes an integration layer for the currently available energy infrastructure and allows a near real-time coordinated control of the produced and consumed energy in a city. The software modules implement innovative simulation and prediction models, both online and offline, as well as optimization algorithms that leverage a more effective and rational usage of all resources available in a city. SCANERGY platform enables a more efficient usage of renewable energy, the reduction of Greenhouse Gas Emissions (GGE), including the quantification of GGE reduction, the improvement of Indoor Air Quality (IAQ) and other comfort indicators. A high-level integrated computational management unit makes use of all data and information that is available in real-time in the energy management systems existing in a city. SCANERGY comprises the following features:
• Integrated intelligent automation of energy trade by using intelligent algorithms;
• Power generation management and optimization (local electric and heat generation);
• Capability of adaptation by learning patterns of behaviour combining all the aspects on management of energy consumption (demand), generation (supply) and storage (stock);
• Smart energy trade between prosumers, based on the local supply and demand of electricity and the preferences of individual prosumers.
• Energy investment simulation to assist management decisions concerning investments in energy efficient equipment (consumption) or renewable energy systems (generation);
The intelligent algorithms, power generation management, energy storage management, learning patterns, energy invest decision patterns and others, have been developed through methodologies and tools of artificial intelligence, data mining and data fusion.
Moreover, we have developed a data base and numerous ICT modules split up in three levels, as well as a middleware, supporting user interaction, including a network of multi-agents to allow agents in different levels to communicate and interact. Multi-agent systems are composed of multiple intelligent actors (or agents) that cooperate, coordinate and communicate between each other in pursuit of common goals. Some of their advantages against other systems, in the context of SCANERGY, are:
Modularity. Features the use of interchangeable units, tailored to the specific scenarios and preferences of agents.
• Scalability. Allows the system to be enlarged to accommodate the growing need for energy management.
• Efficiency. All processes operate in parallel preventing typical bottlenecks.
• Reliability. Failing nodes will not affect the performance of other system components.
• Flexibility. Agents can be inserted and removed dynamically, allowing an open energy market.
• Reactivity: Agents perceive their environment and respond in a timely fashion to changes that occur in it.
• Pro-activeness: Agents are able to recognize opportunities and exhibit goal-directed behaviour.
The modular aspect of SCANERGY allows to apply appropriate modules to different settings in order to provide a system that adequately fits the heterogeneous nature of the energy management in smart grids. The scalability of SCANERGY naturally arises from the combination of different modules and the decentralized coordination between agents. The energy management system can grow as new users join, without reducing its efficiency. In fact, the system exhibits positive externalities, such that the more prosumers join the system, the better its performance becomes. Moreover, this scalability allows the system to adapt not only to individual users, but also to entire neighbourhoods and cities.
SCANERGY resulted in a number of outcomes, the most relevant are:
a) NRG-X-CHANGE mechanism and NRGcoin currency
The newly developed NRG-X-Change concept addresses an observed flaw of the currently deployed incentive mechanism and proposes an alternative using a novel decentralized digital currency called NRGcoin. The new feed-in tariff takes into account the total supply and demand in the neighbourhood, rather than the individual's supply and demand. Only renewable energy that can be consumed is rewarded, while overproduced energy is not. The innovative aspect of NRG-X-Change is that all payments are carried out in NRGcoin, instead of fiat money, which decouples energy generation and producers’ revenue in order to maximize the value of green energy for producers while at the same time make green energy cheaper for consumers.
b) Cloud Market Exchange
The cloud market exchange is the infrastructure built-up in the cloud to handle all the NRGcoins transactions between consumer, prosumers and substations. The aim of the cloud market is to make accessible the bids and offers of a prosumer/consumer to any other consumer/prosumer in a transparent way and through the continuous double auction algorithm match the bids and offers. The cloud market has an API that makes possible the communication with any Raspberry Pi (or Smart Meter) with internet connection.
c) enControl Prosumers
A commercial IoT platform enControl has been upgraded to handle production data and connection to the cloud market. enControl Prosumers will have new features to monitor production of a house, advises to leverage local production and information about the NRGcoin transactions.
d) Prediction and Data Extraction Engine
A prediction engine has been built-up to assist the bid and offers strategies. The prediction engine is able to perform high accuracy forecasting thanks to a new algorithm based on the Fuzzy Inductive Reasoning technique. The prediction engine is able to handle data from different type of sources and predict using the stream of real-time scenarios. In addition, due to its low computational cost, it can be run in devices with limited RAM, such as Raspberry Pi. The prediction engine has the feature to create energy behaviour masks. These masks can be used by any other house with similar characteristics, or combine important features of the mask to achieve better predictions.
e) nAssist Big Data middlware
The nAssist middleware has been upgraded with a new technology (hadoop) that allows the data extraction of massive amount of data. In addition, it has incorporated also a data cleaning module that detects outliers and gaps in the data and correct them.