EDGE Meeting February Reza Ahmadi Kordkheili Birgitte Bak-Jensen Jayakrishnan R. Pillai

Project Overview 2

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4 MV Grid WP2 Distribution Grid Control WP4 Grid Balancing WP2 Distribution Grid Control WP4 Grid Balancing Primary Project Description Modification on the Project

5 WP2: Simulation of distribution network with residential loads; Adding solar PV panels to distribution networks; Adding electric vehicles (EVs) to distribution network; Control ideas and demand management with EVs; Aggregation of the distribution network;

6 MV Grid: Simulation of CIGRE MV grid with residential loads and industrial loads; Simulation of the MV grid with solar PV panels and EVs; Adding wind turbines to MV network; Adding CHPs to the MV network; Setting control ideas for the MV grid with/without EVs in the grid; Evaluating the grid for different winter and summer scenarios; Evaluating the grid for different “wind” scenarios;

7 WP4: LFC model for different grid players, including:  CHP  Wind Turbines  Solar panels  EVs Simulation of the LFC for the base case; Simulation of the LFC with participation of the MV grid;

Part I RES Penetration in MV Grid 8

Contents:  Grid Model  Proposed Algorithm  RES Scenarios  Simulation Results 9

Grid Model 10 Fig. 1. Grid Layout.

11 Grid Model Node Apparent Power (kVA)Power Factor ResidentialIndustrialResidentialIndustrial T ABLE I. D EMAND ON E ACH G RID B US.

Grid Model  Load Modeling: 12 Fig. 2. Typical demand profile for residential and industrial load (p.u.).

Grid Model 13  Load Modeling: EVs: a) commuter.b) family car. Fig. 3. DP of EVs in the grid.

Grid Model Solar Panels: 14 Year Eavg (kWh) Pavg (kW) T ABLE II. A VERAGE P OWER AND E NERGY OF A 3- K W P ANEL.

Grid Model 15 Wind Turbines: Rated Power2000 kW Cut-in speed (Vc_in)3 m/s Rated speed (Vr)11 m/s Cut-out speed (Vc_out)20 m/s Hub height125 m T ABLE III. D ETAILS OF THE W IND T URBINE. Year Eavg (MWh) Pavg (kW) T ABLE IV. A VERAGE P OWER AND E NERGY OF A 2-MW W IND T URBINE.

Proposed Algorithm  Without EVs: 16 Fig. 4. Calculation process without EVs.

Proposed Algorithm  Without EVs: 17 The algorithm gets the data of the wind, i.e. wind speed and wind turbine characteristics. Then, it takes the location of the wind turbines in the grid. Based on these data, it calculated the wind power production in different parts of the grid. For the solar panels, the data of solar panels together with their location act as input for the algorithm. Based on these values, the effect of solar panels on the grid will be calculated. Considering the wind power production and solar production in the grid, and based on the grid load data, the algorithm sends a signal to CHPs to participate them in power production with changing their set points.

Proposed Algorithm  With EVs: 18 Fig. 5. Grid analysis in presence of EVs. Fig. 6. The interaction between the grid and EVs.

Proposed Algorithm  With EVs: 19 The output of the ‘PF-1 unit’ acts as an input for the EV unit. The ‘EV unit’, shown in Fig. 6, takes this data and makes decision with respect to the EV location, EV’s current situation (either being idle or being on the road), EV’s SoC, and voltage conditions of the grid. The voltage conditions are checked for each EV. The maximum voltage of the EV feeder (Vmax), as well as the minimum voltage (Vmin) are calculated and compared with the decision criteria (Cond1 and Cond2 in Fig. 6). Before checking the next EV, the ‘PF-2 unit’ updates the calculations with respect to effect of the current EV.

RES Scenarios 20 Day 1Day 2 WinterLow windWindy SummerLow windWindy T ABLE V. D IFFERENT W INTER AND S UMMER S CENARIOS. Fig. 7. Grid demand for summer and winter scenarios.

RES Scenarios 21 NO. Wind turbinesNO. Wind turbines + NO. PV panels Bus No. Case 1Case 2Case 3Case 4Case 1Case 2Case 3Case T ABLE VI. D IFFERENT RES P LACEMENTS IN THE G RID.

Simulation Results 22 WinterSummer Scenario low windwindy daylow windwindy day Base Wind Case Case Solar Wind + CHP Case Case Wind + solar Case Case Wind + solar + CHP Case Case Wind + solar + CHP + EV Case Case T ABLE VII. M AXIMUM V OLTAGE D EVIATION IN THE G RID FOR D IFFERENT W INTER AND S UMMER S CENARIOS.

23 Simulation Results Wind Turbines: It can be realized that when the wind turbines are located at the remote buses, i.e. buses with the distance from the main transformer, the voltage deviation of the buses increases significantly. Case 3 and case 4 in Table VI represent such scenarios. Solar Panels: Most of the solar power production is during the summer, as they produce more than 95% of their annual energy production during the summer time. In summer scenarios with ‘low wind’ (‘Day 1’ of the summer scenario), solar panels helped improve the voltage of the grid and reduce the voltage deviation. However, in ‘windy day’ of ‘summer scenario’, the solar panels have negative effect on voltage deviations and increase the voltage deviations, compared to the cases where only ‘wind’ is in the grid.

24 Simulation Results CHPs: The positive effect of CHPs is more significant in the winter scenarios. The reason is the output power production of CHP units in winter scenarios. Due to the need of the customers in winter scenarios, the set points of CHPs are much higher in the winter time, compared to the summer time. In cases where the wind turbines are located near load centers (case 1 and case 2 in Table VI), the CHPs have a much more significant effect on reducing the voltage deviation. However, in case 3 and case 4 where the wind turbines of feeder 1 are located on remote areas and remote buses, the effect of CHPs on reducing voltage deviations would be less.

Simulation Results EV E FFECT : 25 a) low wind b) windy day Fig. 8. SOC of EVs in case 4 (winter day).

Simulation Results 26 P OWER TRANSFER AT THE PC ( WITH EV IN THE GRID ): Fig. 11. Power exchange at the PCC: winter ‘low wind’ day: case 4. Fig. 12. Power exchange at the PCC: winter ‘windy’ day: case 4.

27 Considering a ‘low wind’ day, as presented in Fig. 11, between 00:00 to 2:00, adding the EVs to the grid has led to increase in power flow from the upper network to the grid. The extra power requirement is due to the charging requirements of the EVs. An interesting fact in Fig. 11 is the effect of EVs on shifting high demand of the grid. In the evening time, i.e. between 16:00 to 18:00, the EVs start discharging and supporting the grid (V2G(-) in the proposed algorithm in Fig. 6). Simulation Results

28 In the ‘windy’ scenario, plotted in Fig. 12, the EVs hardly affect the power exchange at the PCC. During the day time, the EVs cannot have much role in supporting the grid (V2G(+) in Fig. 6). The reason is that the available storage of the EVs is limited, as it mainly depends on the driving requirements and the distance profile (DP) of EVs. Simulation Results

Conclusion The high penetration of RESs in a standard CIGRE MV grid has been analyzed and investigated. Different scenarios, with different placements of wind turbines in the grid, are analyzed. Also, the presence of residential solar PV panels and CHPs in the grid was investigated. Analyzing the cases where solar panels are placed in the grid show that, in most scenarios, the solar panels have had a positive effect on the grid and reduced the voltage deviation among the grid buses. In cases where the wind turbines are located near load centers, CHPs have more significant effect on reducing the voltage deviation. 29

30 Part II LFC Control

31 Contents:  LFC Overall Model  Included units and systems  Simulation Results

32 LFC Overall Model Fig. 1. LFC model in presence of MV grid.

33 Included units and systems Fig. 2. Generator model of the upper network.

34 Included units and systems Fig. 3. CHP model. Fig. 4. Wind model.

35 Included units and systems Fig. 5. Solar panels in LFC. Fig. 6. Inverter of solar panels

36 Included units and systems Fig. 7. EV block in LFC model.

37 Simulation Results Fig. 8. LFC Simulation.

38 Simulation Results Fig. 9. System frequency. Base Case: Fig. 10. Power production of the upper network.

39 Fig. 10. System frequency: windy day. CHP in the LFC: Windy day: Simulation Results

40 Fig. 11. Participation of CHP in LFC: windy day. CHP in the LFC: Windy day: Simulation Results

41 Fig. 12. System frequency: windy day. CHP in the LFC: Low wind: Simulation Results

42 Fig. 13. Participation of CHP in LFC: low wind. CHP in the LFC: Low wind: Simulation Results

Thanks everyone! 43