WP1: Regional Multi-Energy Grids

multienergy

Innovation Challenges

The increased penetration of distributed renewable poses a number of fundamental problems for the regional grids.

  • First, the volatility and non-dispatchability of wind and solar energy requires an increased amount of generation reserves that, paradoxically, may produce an increased level of CO2 emissions.
  • Second, power imbalances and the compensation of voltage-level fluctuations require expensive capital investments and engineering works associated to the upgrade and reinforcement of distribution/transmission grids.
  • Third, the existing control methods were developed for traditional, centralized power systems with a relatively small number of large resources; they are not applicable to systems with a large penetration of dispersed and non-dispatchable generation.

Such problems require a complete re-engineering of the electrical infrastructure with particular reference to the distribution systems since the direct control of every resource is clearly too complex when the number of systems gets large.

Objectives

For the tackling of these challenges:

  • the first objective of this WP is to develop accurate forecasting tools of renewable energy production systems with the granularity of regional grids (i.e., urban/rural contexts). These forecasting tools will be used to evaluate the support of these resources to the ancillary services of electrical distribution grids. This part of the project will include both photovoltaic and wind generation production along with small hydro-power units.
  • The second main research activity will focus on new advanced metering infrastructures for both medium and low voltage grids. The aim is to provide reliable, deterministic and real-time data for the assessment of the distribution grids real-time state.
  • The third main objective of this WP is knowledge of the network state will then serve optimal control algorithms. In particular, this part will assess the potential topologies of two different control architectures (i.e., centralised vs decentralised) as well as the evaluation of the elements to be controlled (i.e., storage systems, demand side management). Concerning the optimal control algorithms, research is expected to be done in the four main item that refers to the interaction of the electrical grid with other energy network (in particular, in the urban context). The final item that will be evaluated concerns the definition of new grid codes with particular reference to the grid accessibility of distributed generation/active customers/storage systems.

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.
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

Highlights

Real-time state estimation of the Lausanne 125 kV sub-transmission network using PMUs

A single platform for the real-time monitoring, protection and control of the grid

Academic partner: EPFL (DESL)  ( lorenzo.zanni@epfl.ch )        Industry partner: Services industriels de Lausanne (SiL)                          Funding: SFOE                              Project duration: 2014-2017 (3 years)
Currently, distribution grids are characterized by a poor level of automation and need to undergo an extensive transformation in the upcoming years. State Estimation (SE) is a power-system situation-awareness functionality that computes the most likelihood state of an electrical grid (i.e., all the grid quantities: voltages, currents, powers) through the statistical processing of the measurements.
With only the use of measurements provided by Phasor Measurement Units (PMUs), a very accurate, fast and frequent execution of SE, called Real-Time State Estimation (RTSE) can be achieved.
In addition to the performance’ improvement of applications that are already using the SE solution (e.g., security analysis), RTSE can support real-time applications, such as protections and fault location.

Goal
This project aims at validating RTSE based on PMUs in a real-scale electrical grid and at demonstrating the capability of RTSE to support hard real-time applications, such as power-system protections.

Results
PMUs and a Phasor Data Concentrator were used, that are developed at EPFL and characterized by high accuracy and low latencies.
RTSE has been successfully implemented and its performance assessed in the Lausanne 125 kV sub-transmission network.
The proposed PMU-based monitoring infrastructure considerably improves the grid-operator visibility of its critical assets.
A fault location method that is based on the RTSE solution was successfully experimentally validated.
The performance of the developed fault location technique is not influenced by the fault type, the network topology (radial or meshed), the neutral treatment and the presence of distributed generation.
The developed solution allows the grid operator to concentrate in a single platform the monitoring protection and control functionalities. This avoids the proliferation of heterogeneous monitoring infrastructures that are dedicated to the solution of a single problem.
The developed fault location technique can be integrated within a Fault Location Isolation and Service Restoration (FLISR) logic that reduces the grid downtime and improves the related performance indices.

Next step
As a next step, this technology will be deployed and tested in a medium voltage distribution grid Energie.

Figure 2

Figure 2 : Map of the 125 kV sub-transmission network of Lausanne highlighting the PMU and PDC locations

Further reading:

A. Derviskadic ; P. Romano; M. Pignati ; M. Paolone, “Architecture and Experimental Validation of a Low-Latency Phasor Data Concentrator,” IEEE Transactions on Smart Grid , to appear 2017.
S. Sarri , L.Zanni , M . Popovic , JY . Le Boudec , M. Paolone ”Performance Assessment of Linear State Estimators Using Synchrophasor Measurements”, IEEE Transactions on Instrumentation&Measurements , 2015.
P.Romano , M.Paolone , “ Enhanced Interpolated-DFT for Synchrophasor Estimation in FPGAs: Theory, Implementation, and Validation of a PMU Prototype” IEEE Transactions on Instrumentation & Measurements, 2014.
M. Pignatii , L. Zanni , R. Cherkaoui , M. Paolone, “Fault location”, IEEE Transactions on Power Delivery, to appear 2017.

Demonstration of a dispatched-by-design architecture for heterogeneous stochastic resources

Control framework to dispatch, peak shaving, congestion management, economic optimization of the consumption by using a battery energy storage system

Academic partner: EPFL (DESL) (fabrizio.sossan@epfl.ch)          Industry partner: Leclanché
Funding: SCCER-FURIES, Canton de Vaud                                   Project Duration: 2014-2016 (2 years)

Dispatching the operation of inherently stochastic resources, such as distributed renewable generation and demand, allows for reducing the amount of regulating power required to operate the grid. This is a key issue for large-scale integration of renewable energy.
Goal
This project aims at demonstrating how utility scale storage can be integrated to achieve dispatched-by-design operation of stochastic resources without the need of complex communication and coordination mechanisms.

Results
For the achievement of this goal, a control framework has been developed for dispatching, peak shaving, congestion management, and economic optimization of the consumption by using a battery energy storage system (BESS). It consists of a day-ahead planning and real-time phase for the actuation of the battery active power set-point.
The concept was validated at the 750~kW/520~kWh Lithium Titanate BESS at DESL, EPFL in the so-called dispatchable feeder setup.
The innovation of this solution lies on the applicability of the dispatchability concept to a set of heterogenous resources; the decrease of the need for regulating power during intra-day grid operation; the fully decentralized control with minimal coordination requirements; and the model predictive control by including electrochemical models of the battery.

Next step
As an next step, the control framework will embed multiple controllable elements, such as other batteries or shiftable demand.

  Figure 5a : The dispatchable feeder setup (dashed line)
Figure 5b : Day-ahead operation. Forecasted prosumption of 5 EPFL buildings equipped with rooftop-PV panels in terms of expected value, prediction intervals and dispatch plan (dashed and shaded area; and thick line). Figure 5c : Prosumption realization (full line) and the power flow at the grid connection point (dashed line), corrected by controlling the battery power injection.


Further reading:

F. Sossan , E. Namor , R. Cherkaoui and M. Paolone, “Achieving the Dispatchability of Distribution Feeders Through Prosumers Data Driven Forecasting and Model Predictive Control of Electrochemical Storage,” in IEEE Transactions on Sustainable Energy, vol. 7, no. 4, pp. 1762-1777, Oct. 2016.
E. Namor , F. Sossan , R. Cherkaoui and M. Paolone. “Load Leveling and Dispatchability of a Medium Voltage Active Feeder through Battery Energy Storage Systems: Formulation of the Control Problem and Experimental Validation”. ISGT Europe 2016, Ljubljana, Slovenija , October 9-12, 2016.


SMMC – Study on mesoscale-microscale coupling strategies for wind energy prediction improvement

Models for wind energy prediction for researchers, planners and operators in the Wind Energy field

Academic partner: EPFL (WIRE) (jiannong.fang@epfl.ch)         Funding: SFOE, EOS Holding
Project duration: 2015-2016 (1.5 years)

Wind energy prediction over complex terrain can be significantly improved if both large scale weather conditions and microscale topography effects can be captured in numerical models.

Goal
This project focuses on the investigation of strategies of coupling a mesoscale weather model with a microscale computational fluid dynamics model, and performance of case studies to verify and validate the proposed coupling approaches. This will allow the improvement of wind energy prediction over complex terrain and their impact on the grid.

Results
A simple strategy was developed to couple the mesoscale Weather Research and Forecasting (WRF) model with the Large-Eddy Simulation (LES) code developed by the SCCER-FURIES partner. Preliminary test of the coupled WRF-LES framework was performed in the Juvent wind farm at the Jura mountains.
This tool predicts efficiently and accurately the wind and power output over complex terrain. These make it a powerful tool for assessing wind energy potential, and also for optimizing the design, operation and integration to the grid of wind farms.

Next step
The coupled mesoscale/microscale model is currently under further development and its applications to more case studies are on-going.

Figure 10 – Visulization of the terrain model and wind turbines for the Juvent wind farm at the Jura mountains, where the coupled WRF-LES framework has been tested.

Further reading:

A. Niayifar and F. Porté-Agel . A new analytical model for wind farm power prediction, in Journal of Physics: Conference Series, vol. 625, p. 012039, 2015.
J. Fang and F. Porté-Agel . Intercomparison of terrain-following coordinate transformation and immersed boundary methods for large-eddy simulation of wind fields over complex terrain, accepted in Journal of Physics: Conference Series, 2016.

Modelling a domestic heat pump in application to demand side management and explicit set-point control

A control-oriented model of the conversion system to benefit from low tariff periods and environmental heat gains (i.e. improved COP)

Academic partners: EPFL (DESL, LCA2, IPESE) (francois.marechal@epfl.ch)   /   EPFL (Colin Jones, LA3)
Funding: NRP 70               Project duration: 2016-2020 (4 years)

Heat pumps represent a highly attractive solution to reduce both the energy consumption and the greenhouse gas emissions released for the provision of low temperature service requirements. However, for the impact assessment of the heat source/sink quality on the unit performance and thus on the required power demand, optimal control methods are required to fully exploit the potential of the latter conversion system.

Goal
This project hence aims at developing novel multi-dynamic models for prediction of inputs and outputs, and control of domestic heat pumping systems. These models reflect the needs and interests of both the user and the grid operator.

Results
A control-oriented dynamic model of the standard heat pump was developed which can be implemented in both a predictive control framework for demand side management and an explicit set-point control method for active grid operation. The model parameters are fitting from actual measurements of a 9.3 kW (A2/W35) air-water heat pump installed at the DESL lab.
The proposed approach provides building service users and potential microgrid operator with non-intrusive (i.e. without requiring any additional monitoring instruments) model formulations which can be easily adapted to each heat pumping system to provide both fast power set-point predictions and dynamic thermal responses to the interested parties

Next step
As a next step of this project, the defined models are implemented through model predictive control and explicit set-point control to validate the models for real-time applications.

Figure 11: Static polynomial-based model fitting on the active power consumption measurements of a on/off controlled air-water heat pump

Further reading:

P. Stadler , A. Ashouri and F. Maréchal . Model-based optimization of distributed and renewable energy systems in buildings, in Energy and Buildings, vol. 120, p. 103-113, 2016.
A. Ashouri , P. Stadler and F. Maréchal , “Day-ahead promised load as alternative to real-time pricing,” 2015 IEEE International Conference on Smart Grid Communications ( SmartGridComm ), Miami, FL, 2015, pp. 551-556.

SMMC – Study on mesoscale-microscale coupling strategies for wind energy prediction improvement

Models for wind energy prediction for researchers, planners and operators in the Wind Energy field

Academic partner: EPFL (WIRE) (jiannong.fang@epfl.ch)         Funding: SFOE, EOS Holding
Project duration: 2015-2016 (1.5 years)

            Read More

Wind energy prediction over complex terrain can be significantly improved if both large scale weather conditions and microscale topography effects can be captured in numerical models.

Goal
This project focuses on the investigation of strategies of coupling a mesoscale weather model with a microscale computational fluid dynamics model, and performance of case studies to verify and validate the proposed coupling approaches. This will allow the improvement of wind energy prediction over complex terrain and their impact on the grid.

Results
A simple strategy was developed to couple the mesoscale Weather Research and Forecasting (WRF) model with the Large-Eddy Simulation (LES) code developed by the SCCER-FURIES partner. Preliminary test of the coupled WRF-LES framework was performed in the Juvent wind farm at the Jura mountains.
This tool predicts efficiently and accurately the wind and power output over complex terrain. These make it a powerful tool for assessing wind energy potential, and also for optimizing the design, operation and integration to the grid of wind farms.

Next step
The coupled mesoscale/microscale model is currently under further development and its applications to more case studies are on-going.

Figure 10 – Visulization of the terrain model and wind turbines for the Juvent wind farm at the Jura mountains, where the coupled WRF-LES framework has been tested.

 

Further reading:

A. Niayifar and F. Porté-Agel . A new analytical model for wind farm power prediction, in Journal of Physics: Conference Series, vol. 625, p. 012039, 2015.
J. Fang and F. Porté-Agel . Intercomparison of terrain-following coordinate transformation and immersed boundary methods for large-eddy simulation of wind fields over complex terrain, accepted in Journal of Physics: Conference Series, 2016.