The objectives will be achieved through the implementation of specific activities. During Phase I (2014-2016), these activities were focus on the development of new innovative solutions which are validated during Phase II (2017-2020) in demonstration sites.
The activities for each Phase and the associated milestones are presented below.
Activities Phase II (2017-2020)
S 1.1 Advanced Monitoring Infrastructure and Technologies in real-scale Distribution Grids
Subtask leader: 1.1 EPFL-DESL, Prof. Paolone
Description: Implementation in selected demonstrators of monitoring technologies for real-time situation awareness, which range from state estimation to lines and components fault detection and location; assessment of power quality based on measurement campaigns; creation of historical measurement databases to identify prosumer forecasting models.
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M1.1.1 Implementation of grid monitoring infrastructure to achieve situation awareness of medium voltage and low voltage networks |
EPFL-DESL, Prof. Paolone; USI-ICS, Prof. Krause |
Dec.18 |
| M1.1.1a Eye on the grid established (Arbon Demo) | USI-ICS, Prof. Krause | Dec.17 |
| M1.1.1b Asset monitoring study (topology, voltages, currents and flows) @50 fps (RE Demo) | EPFL-DESL, Prof. Paolone | Dec.18 |
| M1.1.2 Measurements campaigns for power quality assessment and stability analysis at a large penetration of grid-connected converter | BFH – ESL, Prof. Höckel | Jun.20 |
| M1.1.2a Power quality and grid impedance measurement campaigns | BFH – ESL, Prof. Höckel | Mar.19 |
| M1.1.2b Power quality studies on LV and MV grid | BFH – ESL, Prof. Höckel | Jun.19 |
| M1.1.2c Analyzing instabilities with inverters in grid-connected and islanding mode | BFH – ESL, Prof. Höckel | Jun.20 |
| M1.1.3 Data-driven identification of electrical grid models | EPFL-LA, Dr. Karimi | Dec.19 |
| M1.1.3a Identification and Validation of dynamic grid model for distributed controller strategy | EPFL-LA, Dr. Karimi | Dec.19 |
| M1.1.4 Models for estimating the flexibility potential in distribution networks |
SUPSI-IASBE, Prof. Rudel; HES- SO VS, Prof. Munch;EPFL-DESL, Prof. Paolone; FHNW, Prof. Gysel; and EPFL-PVLAB, Prof. Ballif |
Dec.20 |
| M1.1.4a Integration of an emulation of a PSP on the Gridlab | HES- SO VS , Prof. Munch | Dec.17 |
| M1.1.4b Determination of the potential (theoretical and practical) of flexibilisation of electricity demand) (RE Demo) | EPFL-PVLAB, Prof. Ballif | Jul.18 |
| M1.1.4c Development of models for prosumers behavior identification (RE Demo) | EPFL-DESL, Prof. Paolone | Jan.18 |
| M1.1.4d Estimation of flexibility in a DSM system (RE Demo) | SUPSI-IASBE, Prof. Rudel; FHNW, Prof. Gysel | Dec.20 |
| M.1.1.5 Data Analysis on Arbon LV-network-data performed, several use cases | USI-ALaRI, Prof. Malek | Dec.18 |
S 1.2 Control Strategies for Real-Scale Distribution Grids, from RT to Daily Energy Balance
Subtask leader: 5.1 SUPSI-IASBE, Prof. Rudel
Description: Deployment in selected full and small-scale demonstrators of local control strategies for feeders’ voltage control, congestion management policies, self-consumption of locally generated renewable energies and energy balance schemes; and for aggregate heterogeneous resources for the provision of ancillary services to the upper grid layer (e.g. primary frequency regulation, voltage control for transmission network, provision of regulating power).
Subtask leader: 5.1 SUPSI-IASBE, Prof. Rudel
| M1.2.1 Identification of grid reduced-order models and data aggregation for local grid control | USI-ALaRI, Prof. Malek; and ZHAW, Prof. Korba | Dec.18 |
| M1.2.1a Classification of distribution grids into general categories (voltage control) | ZHAW, Prof. Korba | Jun.17 |
| M1.2.1b Development of methods for the estimation of most cost-effective solution for a specific grid category (voltage control) | ZHAW, Prof. Korba | Jun.18 |
| M1.2.1c The data selection and aggregation for local control and DSM is established (Arbon Demo) | USI-ALaRI, Prof. Malek | Dec.19 |
| M1.2.2 Formulation of decentralized real-time control strategies for electrical distribution networks |
EPFL-LA, Dr. Karimi; EPFL-LCA2, Prof. LeBoudec and EPFL-DESL, Prof. Paolone |
Dec.18 |
| M1.2.2a Successful communication and control of one DSM device with the use of Commelec agent in 1 feeder (report) | EPFL-LCA2, Prof. LeBoudec; EPFL-DESL, Prof. Paolone | Aug.18 |
| M1.2.2b Experimental validation of voltage control of inverter interfaced grids in grid connected mode (with the use of BESS) | EPFL-LA, Dr. Karimi | Dec.18 |
| M1.2.3 Implementation and experimental validation of real-time control strategies for heterogenous resources at medium and low voltage level |
EPFL-DESL, Prof. Paolone; EPFL-LA, Dr. Karimi; EPFL-LCA2, Prof. LeBoudec; and BFH – ESL, Prof. Höckel |
Dec.20 |
| M1.2.3a Control strategies and stability analyzes for controllable low voltage elements in a real scale LV grid ( small scale demonstrator) | BFH – ESL, Prof. Höckel | Sep.19 |
| M1.2.3b Successful response of the entire feeder with the use of Commelec agents (RE Demo) | EPFL-DESL, Prof. Paolone; EPFL-LCA2, Prof. LeBoudec | Feb.20 |
| M1.2.3c Successful deployment and impact measurement of Commelec -based control on MV | EPFL-DESL, Prof. Paolone; EPFL-LCA2, Prof. LeBoudec | Dec.20 |
| M1.2.3d Experimental validation of frequency and voltage control of inverter interfaced grids in islanded mode (with the use of BESS) | EPFL-LA, Dr. Karimi | Dec.20 |
| M1.2.4 Development of demand-side energy management systems and flexibility interfaces and deployment in experimental demonstrators (actuation layer) |
HES-SO- VS-ISI, Prof.Gabioud; FHNW, Prof. Gysel; and FHNW, Prof. Schulz |
Jul.19 |
| M1.2.4a Communication specification GridEye and DSM (RE Demo) | FHNW, Prof. Gysel | Dec.17 |
| M1.2.4b First results of control of DSM Units by GridEye (RE Demo) | FHNW, Prof. Gysel | Dec.18 |
| M1.2.4c Laboratory prototype of a Customer Energy Management System ( ewz GridLab Demo) | HES-SO- VS-ISI, Prof.Gabioud | Jul.18 |
| M1.2.5 Implementation and experimental validation of demand-side energy management strategies and assessment of their performance (algorithms layer) |
HES-SO- VS-ISI, Prof. Gabioud; SUPSI IASBE, Prof. Rudel; EPFL-PVLAB, Prof. Ballif; FHNW, Prof. Gysel; and FHNW, Prof.Schulz |
Dec.20 |
| M1.2.5a Experimental demonstration of decentralized demand side control strategies (RE Demo) | SUPSI IASBE, Prof. Rudel | Jul.18 |
| M1.2.5b Assessment of the performance of the decentralized optimization algorithms based on 1 year data (RE Demo) | SUPSI IASBE, Prof. Rudel | Aug.18 |
| M1.2.5c Performance assessment of distributed DSM algorithms that use communication and new forecasting models (RE Demo) | SUPSI IASBE, Prof. Rudel | Aug.19 |
| M1.2.5d Models for the optimization of grid penetration of smart DSM in different grid topologies | SUPSI IASBE, Prof. Rudel | Dec.20 |
| M1.2.5e Security report on the DSM system (RE Demo) | FHNW, Prof. Gysel | Dec.19 |
| M1.2.5f Definition of optimal control of DWH for self-consumption strategies (RE Demo) | EPFL-PVLAB, Prof. Ballif | Dec.20 |
| M1.2.5g Demonstration of CEMS | HES-SO- VS-ISI, Prof. Gabioud | Dec.19 |
| M1.2.5h Assessment of CEMS | HES-SO- VS-ISI, Prof. Gabioud | Dec.20 |
| M1.2.5i Simulations on central DSM are operational, battery storage included (Arbon Demo) | FHNW, Prof.Schulz | Jul.20 |
| M1.2.6 Development, emulation and control of new small-scale hydropower storage and generation plants. | EPFL-LMH, Prof. Avellan; and HES- SO VS , Prof. Munch | Dec.20 |
| M1.2.6a Chapter(s) on the Five studies on potential projects of PSP | HES- SO VS , Prof. Munch | Dec.18 |
| M1.2.6b Chapter(s) on the Identication of equivalent battery model for PSP | HES- SO VS , Prof. Munch | Dec.19 |
| M1.2.6c Chapter(s) on the Demonstrator of a small hydropower plant cluster on water utility network | EPFL-LMH, Prof. Avellan | Dec.20 |
S 1.3 Forecasting Tools for Regional and Local Energy Systems
Subtask leader: 1.1 EPFL-DESL, Dr. Sossan and 1.3 EPFL-WIRE, Dr. Fang
Description: Development and validation of forecasting models for power consumption, generation and prosumers behaviour considering various aggregation levels (appliance, household/commercial, LV network, MV network), various prediction horizon lengths and various mixes of distributed energy resources.
| M1.3.1 Improvement of numerical weather prediction at local scale using aggregated low-quality sensor data | 5.1 SUPSI IASBE, Prof. Rudel | [June 2019] |
| M1.3.1a Increase local weather forecast accuracy using aggregated (low-quality) sensor data | 5.1 SUPSI IASBE, Prof. Rudel | [June 2019] |
| M1.3.2 Development and Validation of a multi-scale forecasting framework for wind generation against advanced wind tunnel and field data | 1.3 EPFL-WIRE, Prof. Porte-Agel | [Dec 2019] |
| M1.3.2a Further development of the multi-scale modeling frameworks (started in phase 1) for renewable energy prediction | 1.3 EPFL-WIRE, Prof. Porte-Agel | [Dec 2019] |
| M1.3.2b Validation of the multi-scale modeling frameworks against advanced wind tunnel and field data | /1.3 EPFL-WIRE, Prof. Porte-Agel | [Dec 2019] |
| M1.3.3 Development of optimization tools for the design of wind power plants in application to the verification of Swiss 2050 scenarios | 1.3 EPFL-WIRE, Prof. Porte-Agel | [Dec 2020] |
| M1.3.3a Development of optimization tools for the design, operation and integration to the grid of renewable energy power plants | 1.3 EPFL-WIRE, Prof. Porte-Agel | [Dec 2020] |
| M1.3.3b Application of the new modeling and optimization tools to selected case studies relevant to Swiss Energy 2050 strategies | 1.3 EPFL-WIRE, Prof. Porte-Agel | [Dec 2020] |
| M1.3.4 Short-term forecasting of PV generation and electrical demand at a low aggregation level | 1.1 EPFL-DESL, Prof. Paolone; and 5.UPSI IASBE, Prof. Rudel 1 S | [Dec 2020] |
| M1.3.4a Algorithms and models for the prediction of the day ahead energy demand of households and the distribution grid (24hours) (RE Demo) | 5.1 SUPSI IASBE, Prof. Rudel | [Dec 2017] |
| M1.3.4b Tools for multi time horizon PV point forecasting and prediction intervals | 1.1 EPFL-DESL, Prof. Paolone | [Dec 2019] |
| M1.3.4c Validation of an advanced control algorithms based on short-term forecasting of PV generation (RE Demo) | 1.1 EPFL-DESL, Prof. Paolone | [Dec 2019] |
S 1.4 Planning Strategies for Distribution Grids and Multi-Energy Systems
Subtask leader: 1.6 EPFL-IPESE, Prof. Marechal
Description: Identification and validation of optimal planning strategies for local electrical and multi-energy systems to achieve maximum penetration of renewable generation and maximum capacity of ancillary service provision. Identification of hydro storage potential in typical Swiss regional scenarios and of new turbines able to harvest local hydro potential.
| M1.4.1 Planning of electricity and multi-energy systems interfaced with medium and low voltage grids | EPFL-IPESE, Prof.Marechal; EPFL-PVLAB, Prof. Ballif; ETHZ-FEN, Dr.Demiray; and BFH – ESL, Prof. Höckel | Dec.19 |
| M1.4.1a Design of sizes for batteries, heat storage, fuel cells or heat pumps, etc as a function of the evolution of the grid (RE Demo) | EPFL-IPESE, Prof.Marechal; EPFL-PVLAB, Prof. Ballif | Jun.18 |
| M1.4.1b Best investment strategies when prosumer capacities are increased in the grid (new users, new PV installations, etc.) (RE Demo) | EPFL-IPESE, Prof.Marechal; EPFL-PVLAB, Prof. Ballif | Dec.19 |
| M1.4.1d Tools and Guidelines for target grid planning in the LV and MV grids | BFH – ESL, Prof. Höckel | Jun.18 |
| M1.4.2 Assessment of the hydropower renovation potential (energy production and stability of the grid) | EPFL-LMH, Prof. Avellan | Dec.19 |
| M1.4.2a Assessment of the hydropower renovation potential (energy production and stability of the grid) | EPFL-LMH, Prof. Avellan | Dec.19 |
| M1.4.3 Assessment of investment costs of the different proposed DSM technologies and comparison with grid refurbishment in collaboration with CREST | SUPSI IASBE, Prof. Rudel | Dec.20 |
| M1.4.3a Assessment of investment costs of the different proposed DSM technologies and comparison with grid refurbishment (RE Demo) | SUPSI IASBE, Prof. Rudel | Dec.20 |
| M1.4.4 Guidelines for large-scale deployment of PV generation | EPFL-PVLAB, Prof. Ballif; EPFL-DESL, Prof. Paolone; and EPFL-IPESE, Prof. Marechal | Dec.19 |
| M1.4.4a Study of the targeted feeders’ (Onnens/Rolle) operational limits (RE Demo) | EPFL-DESL, Prof. Paolone | Dec.17 |
| M1.4.4b Sizing and siting of a utility scale and distributed battery energy storage system (RE Demo) | EPFL-DESL, Prof. Paolone | Dec.17 |
| M1.4.4c Operation of the battery storage systems for grid control, feeder dispatching (RE Demo) | EPFL-DESL, Prof. Paolone | Dec.17 |
| M1.4.4d Deployment recommendation for large penetration of PV and distributed storage (RE Demo) | EPFL-PVLAB, Prof. Ballif; EPFL-IPESE, Prof. Marechal | Dec.19 |
S 1.5 Ancillary services to the bulk power systems
Subtask leader: 1.9 EPFL-PWRS, Dr. Cherkaoui
Description: Identification and quantification of the potential of regional energy systems to provide ancillary services to the bulk grid by defining the interfaces for abstracting the flexibility of sets of heterogeneous resources and formulating suitable control algorithms for local energy systems to provide services to the upper grid layer. These activities will provides inputs to WP1 subtask 4 concerning optimal expansion of distribution systems to maximize the capacity of regional systems to provide ancillary services to the bulk grid.
| M1.5.1 Modelling and optimization of system-wide ancillary services provision and system impact | EPFL-PWRS, Dr. Cherkaoui; and HES-SO-EIA-FR, Prof. Favre- Perrod | Dec.19 |
| M1.5.1a Definition of schemes and needed coordination for the provision of ancillary services from small distributed resources | EPFL-PWRS, Dr. Cherkaoui; and HES-SO-EIA-FR, Prof. Favre- Perrod | Dec.18 |
| M1.5.1b System-wide modelling and optimization of ancillary services provision and system impact | EPFL-PWRS, Dr. Cherkaoui; and HES-SO-EIA-FR, Prof. Favre- Perrod | Dec.19 |
| M1.5.2 Demonstration of distributed provision of ancillary services | HES-SO-IESE, Prof. Carpita | Dec.20 |
| M1.5.2a Validation of the model on the demonstration (RE Demo) | HES-SO-IESE, Prof. Carpita | Dec.20 |
Activities Phase I (2014-2016)
Milestones
| M1.1.1. |
Definition of new architectures for the real-time monitoring of low and medium voltage electrical grids based on the use of Phasor Measurement Units coupled with sub-second real-time state estimators
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| M1.1.2. |
Experimental demonstrators of real-time monitoring infrastructures of electrical distribution grids
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| M1.1.3. |
Grid monitoring and assessment procedures based on data analysis
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Leading Institute
Contributing Institutes
Industrial Partners
Milestones
| M1.2.1. |
Definition of low-bitrate control mechanisms for the evaluation of the demand side response contribution to distribution grids ancillary services |
| M1.2.2. |
Experimental demonstrators of residential demand side control mechanisms
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| M1.2.3. |
Definition of energy contracting schemes with customers and their impact on distribution grids |
| M1.2.4. | Estimation of the amount of shiftable power (locally and supra-regionally) |
| M1.2.5. | Control algorithms for distribution grids |
Leading Institute :
Contributing Institutes :
Industrial Partners :
Milestones
| M1.3.1. |
Comprehensive models for the evaluation of the contribution to ancillary services of distributed generation and storage |
| M1.3.2. |
Centralised vs decentralised (multi-agent) controllers for active distribution networks
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| M1.3.3. |
Development of concurrent hydraulic, mechanical and electrical engineering of small units featuring fast dynamics and low CAPEX |
| M1.3.4. |
Modelling of the dynamics regional multi-energy grid integrating small pumped storage plants
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| M1.3.5. |
Definition of processes and performances evaluation of hybrid centralised/decentralised controls of distributed generation/storage integrating demand side response/management
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| M1.3.6. |
Set up and validation of benchmark simulation models representing typical Swiss grid topologies
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Leading Institute :
Contributing Institutes :
Industrial Partners :
Milestones
| M1.4.1. |
Development and validation of multi-scale modeling frameworks for renewable energy prediction |
| M1.4.2. |
Development of optimization tools based on the multi-scale modeling frameworks for the design, operation and integration to the grid of wind farms |
| M1.4.3. |
Application of the new forecast and optimization tools to selected case studies |
Leading Institute :
Contributing Institutes :
Industrial Partners :
Milestones
| M1.5.1. |
Algorithms and processes for the optimization of PV installations in terms of technology choice and installation designs to improve grid integration |
| M1.5.2. |
Thermo-economic model of the integration of multi-carrier regional energy conversion systems |
| M1.5.3. |
Geolocalised modelling of the integration of innovative energy conversion technologies |
Leading Institute :
Contributing Institutes :
Milestones
| M1.6.1. |
Survey of the impact of new grid codes on the distribution and transmission grid controls |
| M1.6.2. |
Elaboration of new standards to limit perturbations caused by resonance between electronic energy converters |
Contributing Institutes :
Industrial Partners :