Impacts of EV Penetration in a Residential MicroGrid

Impacts of EV penetration in a residential MicroGrid V.Lioliou, M.Stamoulis, K.Tsatsakis, N.Hatziargyriou Abstract The connection of batteries of plug-in electric vehicles can have serious impact on the distribution grid. The power flow of this connection can be bidirectional, as vehicles can function either as flexible loads (charging) or storage sources (discharging). This dual procedure must be controllable in order to achieve a more economic and optimal grid operation. Within microgrids, the presence of ΕVs has more advantages. They contribute to the optimization procedure allowing a higher and more efficient RES penetration, while smoothing the load curve. In this case study, the impact of EVs in a residential microgrid, during a typical summer day, is examined. Index terms—electric vehicles, battery storage, microgrid, dispersed storage and generation, vehicle-to-grid, energy management.

Introduction This research focuses on the optimal impact of EVs in a Microgrid . Microgrids comprise Low Voltage distribution systems with distributed energy sources, storage devices and controllable loads, operated connected to the main power network or islanded, in a controlled, coordinated way. [N. Hatziargyriou, 2007] The uncontrolled connection of EVs in the grid increases the peak demand, producing technical and economical issues. Considering the EV batteries as microgrid’s controllable components, charging and discharging (V2G procedure) can be executed in an optimal way. Even more EV battery management can be used for balancing load-generation, islanding, black start and regulation in a Microgrid, items which are not examined in our case study. We focus on the optimal impact that EVs can have in a residential grid by their dual function as: 

Flexible loads



Storage devices

EVs as flexible loads, in a microgrid or a commercial building with DGs , help to take advantage of the surplus energy in periods of high renewable energy production and low load demand. Besides the technical an economic benefits, this approach is environmentally beneficial, as the charging procedure is executed by using energy sources of low CO2 emissions, reducing the CO2 footprint of EVs.

Moreover, EVs as storage devices, can offer this stored energy in high price periods, reducing peak demand.

Storage Modelling Equations The batteries of the EV fleet are modelled as if there was one large concentrated battery for the entire vehicle fleet. Thus, the capacity is an aggregated quantity which is equal to the sum of all individual batteries. The charging and discharging technical constraints of the EVs need to be satisfied at each time interval while the user constraints from traffic patterns must be met during the day. In order to model the capability of EVs to behave either as a load or generation element, the following terms must be added as balance equations. Figure 1 shows a simple drawing of the power fluctuations that a domestic consumer has when EV and PV technologies are present.

Figure 1. Representation of the power flows involved when EV and PV technologies are present in a domestic consumer’s profile.

As seen in figure, the power injections from the grid are: Pload  EVs (t )  Pinj (t )  PPV (t )  EVgrid (t )

From this equation the term EVgrid represents the net effect EV units have to the grid, after charging and discharging. So: EVgrid (t )  EVch arg e (t )  EVdisch arg e (t )

Similar to the grid balance, there is also the EV balance which includes the charging procedure from the grid (G2V), the discharging procedure (V2G), and the trip procedure ( EVtrip ). The EV balance is stated as: 24

EVbalance    G 2V (t )  V 2G (t )  EVtrip (t )  t 1

This equation expresses that whatever energy is charged by the EVs the resource will be spent in either transport purposes or in supplying power back to the grid. In order to determine the state of charge for the battery during each period we consider: EVstore (t  1)  EVstore (t )  dEVstore (t ) The value for EVstore must always be equal or greater than the safety limit of battery (SOC 40%) and lower from the maximum capacity of it. However, these constraints do not apply to the term dEVstore which is the change in the state of charge for a specific time interval. dEVstore (t )  EVgrid (t )  EVtrip (t )

In our case EVstore (7)  BatteryCapacity* Num_of_EVs Once the state of charge equation is defined it is important to establish boundaries that indicate when the EVs will store or discharge energy. These equations are: EVch arg e (t )  wc (t )  G 2V (t ) EVdisch arg e (t )  wd (t )  V 2G (t )

Factor w(t) take either values of 0 or 1 or serve the purpose of enabling the time intervals in which it is possible to charge or to return energy back to the electric grid. In our case during the periods of charging from the grid, we manage the charging procedure in a normal way. We have the load curve (either from prediction or historical data) and we apply a control method so:

Pload (t )  EVch arg e (t )  const , t  ch arg ing period The same method is applied during the discharging period for the evening. We formulate the discharging so:

Pload (t )  EVdisch arg e (t )  const , t  disch arg ing period During the trip periods we consider a departure of EVs from the grid during the first hour. This traffic pattern is simulated by a linear distribution and there is a constant departure of 5 EV per quarter. The amount of energy, when PV generation exceeds the load demand, is: t2

Esurplus    PPV (t )  Pload (t )  , [ t1 , t 2 ] when PPV (t )  Pload (t ) t  t1

This amount of energy is used for EV charging from RES

Microgrid study case In Greece a microgrid installed in Meltemi, a holiday camp near Athens, is exploited in several ways as a demo installation for research purposes. For our study, the energy consumption of the houses is considered as representative of the typical residential demand and increased PV installation is assumed. The Microgrid modeled consists of: 

20 houses



PV unit of 125 KW



20 EVs in ‘housewife’??? mode (After each route they return home)



Energy supply from the grid or diesel generator (back-up)

The typical load diagram is based on IEEE RTS data for a residential load. The peak hours are during 20:00-23:00 and the ‘valley hours’ are during the midnight (01:0007:00). The total load profile during a typical day of July:

Figure 2: Total Hourly Load Demand for the Households

The microgrid includes, as a renewable energy source (RES), a photovoltaic park (PV) of 125 KW . The research has been done for a typical day of July, when the maximum efficiency factor of PVs is 0.63. The generation curve from PV units is the following one (source: DESMIE, Attica). Hourly PV Generation

140,00

120,00

Power (kW)

100,00

80,00

60,00

40,00

20,00

0,00 1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour of Day (h)

Figure 3: Hourly PV generation

Electric vehicles modeling Each one of the 20 EVs has a battery of 12 KWh. The safety limit for the State of Charge of each battery is chosen at 40% of its capacity. The energy consumption of a typical EV is 0.16 KWh/Km, as defined at MERGE Project. Electric vehicles belong to the home owners, having the same schedule each day. They do a trip in the morning hours (07:00-09:00) and another one in the afternoon (15:00-17:00).The mean value of the distance they cover is 20Km. Meanwhile they are connected back to the area grid.

Results Analysis Figure 3 shows the optimal impact of EV fleet in the small camp microgrid of our research

Figure 3: Electricity curves of PV generation,Household consumption, Household & EVs consumption

As seen in the blue area of the figure 3, the load of EV charging is controlled and shifted in the period between 24:00-07:00, the valley hours of the load curve. Between 07:00-09:00, period of EV mobility, the load curve is the typical load of the camp, since there are no EVs connected to the grid. The shape of the curve during the trip hours, assumes that there is no immediate departure of any of the EVs. In the third period of the diagram there is an area (black part) in which EVs are charged from the amount of energy generated by PV, that exceeds the load demand from camp houses. So, during the period of high penetration of Renewable Energy Sources, controlled EV charging, serves to avoid the RES energy rejection. The EVs battery storage capacity is used. The fourth period of the figure presents the next trip of EVs (15:00-17:00), which has no effect to the load curve. In the last period of the figure (17:00-24:00) the potential energy supply, that can be injected from the batteries to the grid, until the safety limit of the batteries SOC , are used by the Microgrid control to avoid the peak energy consumption from the grid. So, EVs are used in Vehicle to Grid mode, functioning as storage device and offering the additional energy stored.

As seen, the EV battery injection does not start right after the EVs’s return and plugin. This is due to the fact that the total amount of energy stored in the battery is limited., so it is used in the period of the curve where the peak load demand is higher. It is obvious, that in systems with RES energy rejection during the night, there is a potential of using this energy for EV charging.

EV Storage –Stationary Storage It is obvious that in the microgrid concept and realization, the storage units are essential to serve the optimal microgrid operation. Stationary storage devices involve a high investment cost. By using EV batteries for energy storage in Microgrids, the investment cost is avoided or at least reduced. So the Microgrid operator has an obvious benefit from EV presence and control. On the other hand EV owner gets reward for the high price energy offers in the grid and obviously receives a compensation for the battery degradation caused by its use from Microgrid. A win-win relation is established between the Microgrid-EV owner and moreover, there is a more efficient usage of the battery storage energy, as in the parking period, their use from the grid prevents the battery decay effect.

Conclusion The advantages from the use of EV battery, either as a flexible load or as storage device, are seen in this case study. The optimal management of the Microgrid is supported with a controlled usage of EVs. The EVs charging control can shift these loads in periods of high RES generation and during the valley hours, with many benefits. Moreover, the use of EVs in V2G mode lowers the peak demand, offering a smoother load curve, with limited variations (providing also more efficient use of generation units). Less CO2 emissions from the total energy consumption is achieved. Economical aspects and benefits have to be examined. Additional research about efficient control methods for EVs is essential.