1. WP4 - INERTIA Aggregators Monitoring, Management and Control Hub
T4.1 - DER Flexibility Analysis, Aggregation and Forecasting Module
Hypertech S.A.

2. Task 4.1 Overview
T4.1- DER Flexibility Analysis, Aggregation and Forecasting Module
Duration: 19 Months (M6-M24)
D4.1 DER Flexibility Analysis, Aggregation and Forecasting Module
Report + Software, Public.
Lead Beneficiary: HYPERTECH (10), Involved Beneficiaries: CERTH-ITI (4), ENG (2), PPC (2), ENELSI (2)
The work has just started

3. T4.1 Objectives
To design and develop visual analytics tools that support:
Multi-parameter criteria analysis algorithms and tools of DER flexibility within the Aggregators prosumer portfolio.
Which attributes aspects do we need to deliver per control action.
How can we aggregate the portfolio based on the characteristics.
How can we manage the reliability issues towards the spatial temporal granularity.
Visualization and interaction mechanisms to the Aggregators for multidimensional analysis
How to provide the optimal visualization – control actions of Local Hub portfolio.
Hypothesis based mechanisms for evaluating different Demand Side Management Strategies and visualize the performance of different alternatives.
How can we take this into account different parameters (e.g. capacity credit ) towards the reliability of the consumer portfolio study
Overall, the Visual Analytics module will allow the Aggregator to analyze the data, in
order to forecast and validate the effect of alternative DSM strategies.

4. T4.1 VA – Generic Overview
Combine scientific visualization with interactive information retrieval and data mining
Support the efficient resolution of highly complex and intractable problems that necessitate for closely coupled human and machine analysis
Provide the basis for interactive analytical reasoning, data transformation, mining and analytics reporting
Brings together the processing power of computers with the unique human traits of cognition and insight
Heterogeneous data sources need to be integrated before visual or automatic analysis methods can be applied (e.g. data cleaning, normalisation, grouping, integration). Analyst applies visual or automatic analysis methods. Automated analysis uses data mining techniques to generate models of the original data that can be visualised for evaluation and refinement. Alternating between visual and automatic methods is characteristic for the visual analytics process. Misleading results in an intermediate step can thus be discovered at an early stage, leading to better results and a higher confidence. Findings in the visualisations can be used to steer model building in the automatic analysis.

5. T4.1 VA – INERTIA Architecture
Conceptual Architecture of Visual Analytics Component
VA component as part of the Holistic Framework ( use of a service oriented approach towards the integration of the different modules - INERTIA DSS)
VA component in close correlation with the other main components of DSS tool:
Grid Coordination & DR Activation Module: Consumer (Local Hub) clusters to be activated based on the signals from the extraction of the appropriate DR strategies as these defined by the Grid Coordination & DR Activation Component.
CIM through Multi Agent DER Prosumers Control & Optimization: in order to provide the historical data needed for the extraction of the optimal portfolio of Local Hubs.

6. T4.1 VA – INERTIA Architecture
“INERTIA Visual Analytics Module” High level Architecture
Comments:
Dependencies with
Grid Coordination…. Get the dispatching of the signals to each one of the selected clusters based on the execution of the appropriate DR strategies , Deliver the results from selection procedure in order to estimate the results from the selection
Global MAS Get the dispatching of the signals for each one of the selected Local Hub Agents, Provide the real time information for the operation of the selected Local Hubs in order to define a more concrete forecasting procedure.

7. T4.1 VA Forecasting Component
Forecasting Analysis : Main functionality on VA tool
Feedback for the optimal functionality of the forecasting tool:
Contextual historical data – relevant to DER flexibility (Environmental, Operational, Spatio-temporal characteristics)  CIM (both for training and simulation purposes)
DER Models in order to define the technical characteristics of DERs  TECNALIA
KPI’s in order to set the rules and the operational parameters of VA tools  ENG
Use cases in order to set the useful INERTIA scenarios for VA tools  ENG
HYPERTECH to further examine different algorithms (mainly data mining techniques and correlation on the available data)

8. T4.1 VA – Further Functionalities
Divide the search space to information clusters via appropriate visualization forms
Enhance the recognition of patterns, hidden within spatial and temporal correlation between occupancy, environmental context , DER operation etc.
Provide a flexible set of interactive tools, that, unlike static diagrams, will allow Aggregators to explore the space of parameter values (e.g. per area, per residential type, per Local Hub consumption etc.)
To be further extracted from the INERTIA Requirements analysis – Task1.2

9. T4.1 Initial Steps (M6)
State of the Art Analysis of Visual Analytics ( As part of the work in Task 1.2)
Definition and Scope of Visual Analytics
Visual Analytics Taxonomy and Application Areas
Visual Analytics in Smart Grids Operations
Gaps and opportunities of SoTA with INERTIA Framework
Different techniques on Data Visualization
Multi-Variant Plot Analysis (e.g. 2D analysis of Electricity Load for different single DERs)
Tree Maps (e.g. for space analysis)
Visualization using colored pies and charts
Visualization using box whisker mean plots (BWM) – Monthly & Hourly
Visualization Kiviat diagrams (for design alternatives comparison in respect to specific variables)
Different techniques. To be further examined taking as parameter the INERTIA Aggregator Use
Cases and literature review Optimal selection of techniques for data

10. T4.1 Next Steps (M6-M9)
Literature review on the optimal Visualization Techniques most appropriate for simulation data, performance indicators and underlying relations
Identification of appropriate user (Aggregator) and application specific parametric values and respective degree of system flexibility
Investigation of the best fitting data mining techniques for the optimal integration of multiple Local Hubs in the holistic VA view
Investigation of potential open source Visualization Platforms to be used for INERTIA
Visualization Toolkit (VTK) - Open Source (Creative Commons), C++, 3D computer graphics, image processing and visualization
Tulip Framework (LGPL), C++, Information Visualization Framework dedicated to analysis and visualization of relational data
Performed an internal literature review on the existing Visualization techniques, mainly focused on:
categorization and short description of the most widely known visualization techniques and visual components
analysis of the selection parameters to be considered when choosing the most appropriate visualization techniques
definition of the optimal visualization techniques based on
the type of the data that have to be visualized
The task they are going to be used for
the dimensionality and the scalability of the data to be visualized
suggestion of appropriate visualization techniques to be used for the results of the enterprise performance
simulation based on the metrics and the identified KPIs
Performed an initial overall review on the most widely used Visualization Toolkits. Specifically:
review of the Toolkits / Platforms / Integrated Development Environments (IDEs) that already exist
checked their technical specifications (programming language, architecture etc)
checked their functionality (visualization components and techniques etc)
examined possible existing applications in the domains of Energy and Business process modelling

11. T4.1 Dependencies – Required Contributions (M6-M9)
All involved partners will contribute in relation to the following :
PPC and ENELSI
Provide feedback on Task 1.2 (extraction of Business Requirements for Visual Analytics tool) as the main Aggregators (extraction also from close partners - LL )
Evaluate the initial selection of Visualization Techniques, defined during the literature review
ENG
U&B Requirements (Task 1.2) – Final presentation of U&B Requirements related to the Visual Analytics module
Performance Models (Task 2.4) – Initial review on global performance models to be further integrated on Visual Analytics tool (!!!!)
CERTH
Examine the interfaces of VA tool within the INERTIA DSS (T1.4- INERTIA Architecture)
Initially examine the functionality of SoTA Tools (PANOPTICON solution, PRWEB solution as these mentioned in the DoW) towards their role in the INERTIA Framework

12. T4.1 Dependencies – Required Contributions (M6-M9)
All involved partners will contribute in relation to the following :
HYPERTECH
U&B Requirements (Task 1.2) – In cooperation with ENG for the final extraction of U&B Requirements related to the Visual Analytics module
Functionalities (Sub components, interfaces, relations) of VA module within the INERTIA Architecture (Task1.4) (in cooperation with CERTH)
SoTA work on visualization techniques and initial selection of the best fitting based on their scope in INERTIA Framework (based on the metrics and the identified KPIs)
Initial extraction of the Data models related to the VA tool (in cooperation with TUK)
Definition of the required CIM interfaces (in cooperation with TUK)
A lot of work for the next period

13. T4.1 INERTIA Visual Analytics
Questions??
Thank You !!!