From microgrids to smartgrids

ra day and the real time 
management of microgrids. 
Fig. 20a: PV power profiles of forecasted data 
over 15 minutes and real time measures 
Fig. 20b: Aggregated consumption power profiles 
of forecasted data and real time measures 
Fig. 21: Real power profiles of photovoltaic, battery, consumption 
and grid exchange in tm without intra day re optimizations 
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Fig. 22: Real power profiles of photovoltaic, battery, consumption 
and grid exchange in tm with intra day re optimizations 
Fig. 22 illustrate the actual results of the test, which clearly show a more 
performant control thanks to the combination of re optimization algorithm and the ESS 
in comparison in case without intra day re optimization (Fig. 21). The use of 
re optimization allows to reduce the battery’s solicitations and to restore the pack’s 
SOC in order to reach a final value of 50%. 
2.7. Optimal Charging Scheduling of EV in Microgrid 
Recently, there has been a rapid growth of electric vehicles (EV) connected to the 
grid. Electric vehicles (EVs) play a key rơle in the transition towards a cleaner energy 
future. The intersection of energy and automotive sectors and the Smart Grid potential 
given by electric mobility is followed with great interest. In France in 2020, this 
enthusiasm for electric vehicles will result in the consumption of 4 to 5 TWh for 2 
million electric vehicles. Fig. 23 presents a microgrid includes Electric Vehicles (EV) and 
PV system. 
The connection of a large number of electric vehicles to the grid can raise several 
technical problems or can have significant impacts on power systems. 
G ri d
P g r id P P V
P E V s
Fig. 23: Microgrid with Electrical Vehicle and PV system 
In this part, the management system determines the charging planning of EV in 
order to minimize the total peak consumption. 
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Finally, the objective function is to minimize the total peak consumption of the 
charging station: 
min ቄmaxt∈D ሼ ܲݐ ሽቅ = min ቐmaxt∈D ቐ෍ PEVi Xit
NEV
i=1
ቑቑ 
The simulations are carried out with charging parking in an industrial grid with 
100 electric vehicles with normal charge (3 kW). The capacity of each EV is 25 kWh. 
EV charging can be switched on or switched off each 30 minutes (or 10 min). The 
charging start time is varied randomly with a normal distribution in the parking between 
7am and 9am. The charging stop time is randomly varied between 5pm and 7pm with a 
normal distribution. The initial SOC of EVs is randomly varied between 20 and 80% 
with a uniform distribution. 
Fig. 24: EV total power and power exchange with grid without management 
and with management (without PV production) 
Fig. 25: Evolution of EVs’ SOC (without PV production) 
Fig. 26: EV total power and power exch ange with grid without management 
and with management (with DSO; without PV production) 
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Fig. 27: EV total power and power exchange with grid without management 
and with management (with PV production) 
In Fig. 24, the simulation results show that without management the total peak 
consumption can reach 300 kW between 8:30am and 9:00am. With the proposed 
solution, the total peak consumption can be reduced to 111 kW (black curve). Fig. 25 
presents the evolution of EVs’ SOC over time. After charging stop time Tstop of each 
vehicle, almost all batteries of EV are filled. 
With the proposed solution according to the requirement of DSO (signal received 
from DSO) to reduce total charging power to 50 kW between 11:00am to 12:30pm. 
Before 11:00am and after 12:30pm, the peak consumption is minimized to 129 kW (red 
curve in Fig. 26). Fig. 27 shows the EV total power and power exchange with grid 
without management and with management (with 150 kW of PV production). 
Figs. 28 and 29 present real demonstration with a EV charging station at the 
CEA Grenoble and CEA INES. 
Fig. 28: Real demonstration at CEA Grenoble 
Fig. 29: Real demonstration with a EV charging station at the CEA INES 
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2.8. Energy Management Systems in Buildings 
Actually, it has been observed that the building sector contributes considerably to 
final energy demand. Therefore, many studies have been realized in this area. Our work 
focuses on the smart control of energy management systems in buildings (Fig. 30). This 
paper proposes a structure of the control system in buildings, in particular for a heating 
control. This application uses a new real time control method that allows to reduce 
peak consumption while maintaining thermal comfort. The proposed control method is 
based on wireless sensor network (WSN) technology which offers continuous 
measurement, and an interoperable communication network. This method is validated 
by simulations and real tests. The results demonstrate the performance of the proposed 
solution and its capability to control heating load. This method can be adapted to any 
problems by considering dynamic pricing, signals from energy provider and distribution 
system operator (DSO). 
The proposition relies on a wireless sensor network (WSN) which allows 
simultaneous, real time measurement of indoor temperature and power consumption 
(Fig. 31). The ZigBee communication protocol is used between wireless temperature 
sensors and equipment. 
Fig.30: Energy management system 
Fig.31: Architecture ofthe proposed system for heatingcontrol 
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This system comprises an array of wireless temperature sensors, a wireless 
electrical power sensor, radiators equipped with adaptive controls, and a central control 
unit. The proposed solution is tested for a real apartment to control radiators (Fig. 32). 
Fig. 32: Plan of the test apartment 
Fig. 33: Power required with the traditional heating regulator (R0) 
Fig. 34: Interior temperature of the three rooms with the traditional heating regulator (R0) 
To illustrate the advantages of the proposed method, we applied a 2700 W limit 
for authorized power. Thermal comfort is always maintained, but T_max is adjusted 
according to the instantaneous power consumption. 
Fig. 35: Power required with the proposed method (R1) 
Power required(W)
0
500
1000
1500
2000
2500
3000
3500
4000
14:24:00 16:48:00 19:12:00 21:36:00 00:00:00
Temperature
19
19,5
20
20,5
21
21,5
22
22,5
23
14:24:00 16:48:00 19:12:00 21:36:00 00:00:00
Time
°C
T1
T2
T3
Authorizedpowe
Power required(W)
0
500
1000
1500
2000
2500
3000
3500
4000
14:24:00 16:48:00 19:12:00 21:36:00 00:00:00
Time
W
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Fig. 36: Interior temperature of the three rooms with the proposed method 
These results indicate that: 
 The proposed heating control allows a reduction in total power demand from 
3770 W to 2700 W. 
 Thermal comfort is maintained: the interiortemperature of the rooms remains 
between 22 °C and 20 °C. With the proposed method, Fig. 13 shows that maximal 
temperature is variable, and minimal temperature is always maintained at 20 °C. 
 The reduction of contract power therefore results in a lower cost of 
subscription. 
Temperature was regulated for the two cases over a 24 hour period (from 0h to 
24h). The table below shows that the differences in energy consumption between the 
two cases are negligible. 
 Tmin 
(°C) 
Tmax(°C) Consumption 
(kWh) 
Pmax 
 (W) 
Traditional regulation 20 22 22.843 2700 
Proposed method 20 22 24.84 3700 
2.9. Interoperability of microgrid platforms 
The interoperability of micro grid platforms, particularly in information and 
communication layers has been developed. The implementation of Common 
Information Model (CIM) semantic over OPC Unified Architecture (OPC UA) protocol, 
particularly the mapping of CIM semantic to OPC UA address space, is also considered, 
to ensure that the exchanged data is mutually and correctly understood by all the 
partners of the network. Some insights about requirements to deliver OPC based 
application via WAN connection are provided. This combination brings CIM semantic 
to the OPC UA communication and allows the provision of OPC based applications via 
WAN connection. This contribution enables a seamless and meaningful communication 
among partners of the collaboration network and provides a strong support for 
Variable setpoint 
Temperature
19
19,5
20
20,5
21
21,5
22
22,5
23
14:24:00 16:48:00 19:12:00 21:36:00 00:00:00
Time
°C
T1
T2
T3
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interoperability of micro grid platforms between PRISME (CEA INES) and PREDIS 
(G2elab) (Figs. 37 and 38). 
Fig. 37: Interoperability of micro grid platforms between PRISME (CEA INES) 
and PREDIS (G2elab). 
Fig. 38: SCADA and cloud architecture proposed for interoperability 
between two platforms – PRISME (CEA INES) and PREDIS (G2elab) 
3. DEMONSTRATIONS 
In order to analyze impacts of RES integration and evaluate proposed solutions 
for microgrid, a real time simulator and real demonstration are used at the CEA INES 
(Figs. 39 and 40). 
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Fig. 39: Microgrid with real time simulator at the CEA INES 
Fig. 40: Microgrid demonstration at CEA INES 
4. CONCLUSIONS 
Smartgrids and microgrids are one of the most important activities at the 
CEA/INES, and the lab of intelligent electricity systems (LSEI). The LSEI develops 
management and control strategies of any complex electricity system for efficient smart 
grid integration or stand alone operation. The developed algorithms are on one side 
predictive algorithms which use production and consumption forecasting for energy 
management, and on the other side real time system control algorithms. Proposed 
solutions are evaluated by the demonstration at the CEA INES. 
REFERENCES 
[1] Gabin Koucoi, Quoc Tuan Tran, H. Buttin, “Energy Management Strategies for Hybrid 
PV/Diesel Energy Systems: Simulation and Experimental Validation”, International Journal 
of Energy and Power Engineering. Vol. 5, No. 1, 2016. 
[2] V.H. Nguyen, T. Tran Quoc, Y. Besanger, “SCADA as a service approach for 
interoperability of micro grid platforms”, in Elsevier Sustainable Energy, Grids and 
Networks (2016). 
[3] Quoc Tuan TRAN, Van Linh NGUYEN, “Integration of Electric Vehicles into Industrial 
Network: Impact Assessment and Solutions”, IEEE/PES, General Meeting, Boston, 
Massachusetts, USA 17 21 July 2016. 
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[4] E. Krüger, E. Amicarelli and Q. T. Tran, "Impact of european market frameworks on 
integration of photovoltaics in virtual power plants," IEEE EEEIC, 16 IEEE International 
Conference on Environment and Electrical Engineering, Florence, ITALY, 7 10 June 2016. 
[5] E. Amicarelli, T. Tran Quoc, S. Bacha, “Multi Agent System for Day Ahead Energy 
Management of Microgrid”, European Conference on Power Electronics and Applications 
(EPE ECCE Europe), 5–9 September 2016, Karlsruhe, Germany. 
[6] T. Tran Quoc, “Optimal energy management and control for an isolated area”; CIGRE, 
International Symposium DEVELOPMENT OF ELECTRICITY INFRASTRUCTURES IN 
SUB SAHARAN AFRICA, Cape Town–South Africa –26 30 October 2015. 
[7] N.A Luu, T. Tran Quoc, “Optimal energy management for grid connected microgrid by 
using Dynamic programming method”, IEEE/PES, General Meeting, Denver, Colorado, USA 
27 30 July 2015. 
Ph.D. thesis 
T1. Guillaume RAMI, "Adaptive voltage control for distributed generations connected to 
distribution networks", PhD thesis prepared at the Grenoble INP, Defended on 9 
November 2006. 
T2. Thanh Luong LE “Detection of instability in grid with high RES penetration”, PhD thesis 
prepared at the Grenoble INP, Defended on 22 January 2008. 
T3. Thi Minh Chau LE, "Coupling Photovoltaic inverters to the network, aspects of control 
and support capacity for disturbances", PhD thesis prepared at the Grenoble INP, 
Defended on 25 January 2012. 
T4. Van Linh NGUYEN, "Coupling photovoltaic systems and electric vehicles to the grid: 
Problems and solutions"; PhD Thesis prepared at the Grenoble INP and CEA INES, 
Defended on 1st October 2014. 
T5. Cedric ABBEZZOT, "Flywheel energy storage system coupled to the photovoltaic 
generator and controlled by a real time simulator"; PhD Thesis prepared at the CEA 
INES, Defended on 15 December 2014. 
T6. Ngoc An LUU, "Strategies of control and management for microgrids", PhD Thesis 
prepared at the CEA INES and G INP, Defended on 18 December 2014 
T7. Eiko KRUGER, "Development of algorithms for optimal management of energy storage 
systems based on adaptive models", PhD Thesis prepared at the CEA INES, Defended on 
21 November 2016. 
T8. GABIN A. KOUCOI, "Energy Management in PV/Diesel hybrid system for isolated and 
rural zones: optimization and experimentation”, PhD Thesis prepared at the CEA INES 
and University of Burkina Faso, Defended on 28 February 2017. 
T9. Elvira AMICARELLI, "Management strategy for power grids with a high rate of distributed 
renewable production", PhD Thesis prepared at the CEA INES, Defended on 16 October 
2017. 
596 | HỘI NGHỊ KHOA HỌC VÀ CÔNG NGHỆ ĐIỆN LỰC TOÀN QUỐC 2017 
T10. Hélène CLEMOT, "Strategies for optimal management of marine wave resources ", PhD 
Thesis prepared at the CEA INES and ECN. 
T11. Tung Lam NGUYEN, " Smart control strategies in Microgrids with Multi Agent Systems", 
PhD Thesis prepared at the CEA INES and G INP. 
T12. Karla SOUSA, "Stability in large network with high RES penetration”. 
T13. Laurène PARENT, "Primary and secondary reserve in large network with high RES 
penetration". 
T14. Anthony ROY, "Management of an island grid in SEA". 
T15. Tran The HOANG, "Smart protection strategies in Microgrid". 
T16. Tai LE, "New Architecture of PV power plants ". 
Several Projects 
[P1] GREENLYS: Smart grid demonstration of the management of multiple electric vehicle 
charging stations. 
[P2] REFLEXE: Smart grid demonstration at the regional size (south east of France). 
[P3] PARADISE (ANR PROGELEC Project ID: ANR‐13‐PRGE‐0007): high penetration of 
renewable energy resources into grid with DS. 
[P4] ERIGRID (H2020, project ID: 654113) European Research Infrastructure supporting Smart 
Grid Systems Technology Development, Validation and Roll Out. 
[P5] SEAS (Call 7 ITEA2, No. 1204) Smart Energy Aware Systems. 
[P6] UNITED GRID (H2020, Project ID: 314175): Autonomous Management System 
Developed for Building and District Levels. 
[P7] M2M GRID (ERA‐NET, Project ID: 82136): From micro to Mega GRID: Integration 
approach for the new generation of smart grids. 
[P8] PPInterop (Carnot project): Interoperability between two platforms: PRISME PREDIS. 
[P9] FENIX (European project) Virtual power plants.