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  • Periodic Reporting for period 1 - DREAM-GO (Enabling Demand Response for short and real-time Efficient And Market Based smart Grid Operation - An intelligent and real-time simulation approach)

DREAM-GO Report Summary

Project ID: 641794
Funded under: H2020-EU.1.3.3.

Periodic Reporting for period 1 - DREAM-GO (Enabling Demand Response for short and real-time Efficient And Market Based smart Grid Operation - An intelligent and real-time simulation approach)

Reporting period: 2015-02-01 to 2017-01-31

Summary of the context and overall objectives of the project

DREAM-GO aims at conceiving, developing, implementing, and validating models enabling Demand Response (DR) for short and real-time efficient and market based smart grid operations.
DREAM-GO activities and results will significantly advance the present state of the art by developing: business models for short and real-time DR in smart grids; models and methods to simulate and assess the use of short and real-time DR in smart grids; and specifications for the communications, devices, and methods to enable Direct Load Control (DLC) of consumers’ loads.
DREAM-GO approach takes into account that power system operation and management and the electricity market operation are strongly connected. Missing an adequate modelling of their interrelationships is a strong limitation in the present state of the art; DREAM-GO will significantly contribute to overcome that limitation.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The work has intensely addressed the identification and characterization of existing DR programs, in particular in Europe and in the US. This study has addressed detailed characteristics of the DR programs at the country and System Operator levels. The study has also addressed the relevant standards as well as the remuneration schemes being used. The features and actions related with each involved player, which are required to enable the efficient and widespread use of DR, have been analysed. Market opportunities for DR, considering the foreseen smart grid evolution, have been identified. The results of these studies are being used as the departing point for the conception and development of innovative DR programs, with some new programs already developed and being used for simulation. A Digital framework for Demand Response Registration (D2RD) has been developed and is integrated with the TOOls Control Center (TOOCC), which is also being developed.
Activities have also addressed the conception, development, and implementation of analytic and simulation models. The goal of those models is to simulate the smart grid environment considering the technical and market aspects, in which DR plays a particular very relevant role. DREAM-GO has produced realistic models integrating detailed modelling of the electric networks and SIMULINK based models to be used in real-time hybrid simulation using OPAL-RT.
Significant work has been dedicated to conceive, develop, and implement a complex multi-agent based simulation infrastructure. This infrastructure includes a community of multi-agent systems, putting together computationally simulated elements (in particular, all the available intelligent algorithms used by the involved players), emulated elements (for instance wind emulators, industrial loads adapted to represent real load profiles), and real-world elements (namely, real buildings, loads, photovoltaic panels, etc.) that realistically create an augmented reality environment for smart grids.
A smart city mock-up enabling to visually demonstrate the DREAM-GO models and solutions has been conceived and is under development, with already demonstrable results.
DREAM-GO activities intensively addressed the optimization of the available energy resources, giving DR resources the high importance they should have in the foreseen smart grids. Results already achieved include: clustering models and methods for aggregation and remuneration of DR; models, methods and techniques for forecasting load, wind generation, and photovoltaic generation; optimization models, methods and techniques for energy resource optimization addressing short and real-time horizons; stochastic models for representing renewable energy resources based generation and consumption; non-intrusive load monitoring methods; real-time locational system for efficient energy use; models and implementation of embedded software for consumption monitoring and smart meters, enabling DR features with focus on DLC; study and implementation of OpenADR schemes.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

EU’s huge investment on distributed energy resources with renewable based electricity generation has made European researchers advance on its management and control. As competitive markets have been consolidating their operation throughout Europe, market issues and simulation are also strong topics of European researchers. By raising knowledge on the Demand Response approach in the EU, DREAM-GO expects to influence the discussion on smart grid efficiency, using the project results to promote this discussion among stakeholders, namely by providing SMEs with proper pathways to bring their new products into the market and by developing innovative business models where SMEs and power industry players can cooperate to develop more cost-effective solutions (namely in smart metering). Being focussed on Demand Response, DREAM-GO will ultimately contribute to make electricity consumers more conscientious and active players, with positive impacts on the overall systems efficiency and, consequently, on the competitiveness of the EU economy.
As one of the main results achieved so far, TOOCC allows to choose the existing and developed models and to set up all the necessary parameters to be used in each simulation. The TOOCC enables the efficient use of the real-time simulation platform under development, providing the users with a large set of data and model options, which are available locally and/or remotely.
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