The Virtual Power Plants (VPPs) concept is increasingly spreading among the networks and among electrical utilities. This deliverable starts from the basic definition related to VPPs to pave a basic and shared knowledge of the topics discussed in edgeFLEX. Afterwards, the voltage control scenario, which applies on VPPs, is presented and detailed. This description offers a solid and clear environment in which the voltage control service can be tested and assessed.
The Deliverable describes the voltage control algorithm that has been delivered to WP4 as docker container for integration into the edgeFLEX architecture. The algorithm is based on an online voltage control solution which can be used to control Distributed Generators (DGs) and Energy Storage Systems (ESSs) installed in a distribution grid.
This deliverable describes the second voltage control algorithm developed in the second reporting period of the project. The algorithm is based on a Model Predictive Control strategy and it integrates an interface with the Local Flexibility Market to engage the customers.
The deliverable provides a summary of trends, technical issues and challenges associated withfrequency control and the provision of virtual inertia through nonsynchronous devices. The deliverable also describes the scenarios and provides the data that are utilized to test the VirtualPower Plant (VPP) frequency and inertial response control concepts developed in deliverablesD2.2, D2.3, D2.4, and D2.5. The scenarios are defined based on modified versions of wellestablished benchmark networks suitable for power system frequency and rotor angle stabilityanalyses, where conventional fossilfuel based synchronous generators are replaced by VPPscomposed of nonsynchronous, converterbased energy resources.
This deliverable describes frequency control and metering strategies for virtual power plants connected at the transmission and/or distribution voltage levels. These include coordinated controlstrategies for both primary and secondary frequency control, a measurementbased techniqueto estimate the amount of frequency regulation provided by gridconnected devices, as well asan online inertia estimation method that can be utilized to improve the fast frequency responseof virtual power plants. All algorithms are tested through computerbased simulations accordingto the relevant scenarios defined in deliverable D2.1.
This deliverable describes the frequency control concepts and algorithms developed for future virtual power plants and energy communities. These include a new theoretical concept for modeling, estimation and control of frequency variations; a set of combined voltage-frequency controllers for distributed energy resources and virtual power plants; an aggregated virtual power plant model for system-wide transient stability studies; and a stochastic decentralized control strategy for charging large fleets of plug-in electric vehicles. All algorithms are tested through computer-based simulations according to the relevant scenarios defined in deliverable D2.1
This document presents the technical details of the inertia estimation service developed in work package WP2 as a monitoring tool for system operators. The report discusses the algorithm development procedure and the different test use cases used for validation at proof of concept level.
The document presents the inertial control allocation algorithm developed in work package WP2 as a scheduling tool for system operators and VPPs to allocate frequency response. The report discusses the two-level optimization problem formulation and the different test use cases used for validation at a proof-of-concept level.
D3.1 5G ICT requirements, development and testing for edgeFLEX solution (M30)
In this study, we explore the optimization of virtual power plants (VPP), consisting of a portfolio of biogas power plants, a battery and a set of intermittent sources such as wind and solar. We operate under price and weather uncertainty and to handle it, we employ methods of machine learning. For price modelling, we consider the latest trends in the field and the most up-to-date events affecting the day-ahead and intra-day prices. We demonstrate the performance of the price models by both statistical methods and improvements in the profits of the virtual power plant. Optimization methods will take price and weather forecasts as input and conduct computer solving parallelization, decomposition, and splitting methods to handle sufficiently large numbers of biogas power plants and intermittent sources in a VPP. Finally, we demonstrate the positive social impact of such VPPs and the proposed strategies.
In this report, we explore the optimisation of virtual power plants (VPP), whose goal is to balance a wind park consisting of a portfolio of biogas power plants and a battery, while maximising the VPP revenues. Machine learning methods are employed to handle uncertainty in price and wind production. For price modeling, we consider the latest trends in the field and the most up-to-date events affecting the day-ahead and intra-day prices. The performance of our price models is demonstrated by both statistical methods and improvements in the profits of the virtual power plant.
Optimisation methods will take price and imbalance forecasts as input and conduct parallelisation,decomposition, and splitting methods to handle a sufficiently high number of assets of a VPP to operate the methods. The focus is on increasing the speed of computing optimal solutions to large-scale mixed-integer linear programming problems. The highest increase in computing speed enabled by our method. which we called Gradual Increase. is two orders of magnitude. Further progress in this methodology and its new facets are demonstrated.
This report describes the platform developed in the project enabling the development, testing and deployment of these services and techniques in both field and laboratory trials. It also describes the backbone services allowing the services to be linked and deployed singularly or in groups to enable the development of use cases enabling new ways to interact with and utilise the capability of a VPP.
The goal of the edgeFLEX project is to advance the role of the VPP with the use of advanced grid management techniques, effective optimisation, flexibility provisioning and trading combined with enabling solutions such as Service Level Agreement Monitoring tools, edgePMU devices and 5G capabilities. This report details how these techniques, tools, services and technologies can be delivered to the customer as a Minimum Viable Product so that it can be assessed and refactored and also, used as a tool to gain knowledge relevant to the customer’s needs with respect to the tools and services from the edgeFLEX Platform. It also describes the MVP in the context of the customer, the trials and outlines the role of the MVP and its components to the advancement of the VPP.
The goal of the edgeFLEX project is to advance the role of the VPP with the use of advanced grid management techniques, effective optimisation, flexibility provision and trading combined with enabling solutions such as Service Level Agreement Monitoring tools, edgePMU devices and 5G capabilities. This report details how the MVP developed in the first phase of the project has been further enhanced, with a particular view of the internal interfaces which allow the edgeFLEX platform to interact with Grid Control services and also external services such as VPP optimisation and engaging with Flexibility trading platforms. The report describes these interfaces in terms of functional architecture, technologies and how Policy Based Grid Management and 5G communication features support the operation of these interfaces to enable the advancement of the VPP.
D4.4 Description of assessment of platform control service performance (M30)
D5.5 5G use case vallidation results in laboratory tests (M30)
This document introduces the edgeFLEX approach to combine technological approaches with organizational structures in order to provide more flexibility in the European electrical network, at a moment when the need to mitigate fluctuation in power generation due to the increasing share of intermittent energies in the electrical mix in Europe becomes crucial. On the one hand are described the various technological solutions and sources of flexibility that can be relied on: they consist in central local flexible assets or distributed small flexible assets. On the other hand, are explored organizational structures can be used to foster the harvesting of flexibility; they may be DSO centered, aggregation-service-provider centered or happen thanks to local energy communities.
D6.3 Engaging with policy makers, with organizations and experts in regulation and standardization, V1
This is the initial version of the deliverable reporting on the outcome of the actions taken by engaging with policy makers, with organizations and experts in regulation and standardization, to support the implementation of edgeFLEX technical solutions in the first phase of the activities of the project.
It provides the preliminary analysis of potential project results from the perspectives of regulation and standardization, related both to the existing regulatory framework, to trends and as a response to technology challenges. Starting from a series of initial proposals to update and complete the regulatory framework from the perspective of the technical requirements generated by edgeFLEX solutions, this deliverable describes the project work plan for regulatory and standards work and the steps taken in the consultation process with the relevant stakeholders.
We describe a new business and financing model for variable RES, to enable and simplify investments in RES when the subsidy schemes now come to an end. The existing and new model and mechanism in detail in a study including impact of the new model on different risks and inflow of capital. Finally, we present a framework for the pricing of the various components of a PPA, reformulated in a pure physical operations part and a pure financial part using Green Power Swaps.
This document is the updated dissemination and communication plan for edgeFLEX. It summarizes and structures all completed, running and upcoming dissemination and
communication activities according to the development stage of the project. It gives a comprehensive overview of the strategy, the derived measures and their implementation.
D7.2 Updated plan for disseminatin and communication of results (M30)
D7.3 Report on courses for professional practitioners and academia (M30)
The project has made the progress planned for the first year of its three year duration and has even exceeded the expected level of results in several areas of activity. Research concepts, implementation and platform integration work have progressed well, providing good starting points for the field trials of the project, which will start to become operational later in 2021. The project is well positioned to achieve all its planned goals and KPIs in its coming two years of activity.
The edgeFLEX project has made excellent progress during the second of its three years planned duration towards achieving all of the planned goals of the project. All the project activities have progressed according to the originally planned schedule defined in the Grant Agreement.
D8.8 Description of publishable data sets from experiments and field trials, V1
D8.9 Description of publishable data sets from experiments and field trials, V2
D8.10 Description of publishable data sets from experiments and field trials, V3 (M27)
The two deliverables describe the current plans of the edgeFLEX project on what data sets are planned to be collected in trial sites as well as laboratory experiments, simulations and calculations in the Work Packages (WPs) and then be published and with that be made available for the general public. These reports have been written based on the expected data drawn from project work that was planned to be completed in the first phase of the project and was updated on month 15 based on new findings made during the project year and will be updated again in M27 of the project.
edgeFLEX will conduct field trials of fast and slow dynamics distribution management control services and energy flexibility trading with prosumers in Germany and Italy. Furthermore, the project plans to conduct laboratory trials and simulations based on electricity grid simulated data in Dublin, Bologna and in Aachen.