# EV Aggregators and Energy Storage Units Scheduling into Ancillary Services Markets: The Concept and Recommended Practice

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## Abstract

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## 1. Introduction

## 2. Problem Formulation

_{i}), regulation down (YD

_{i}), and responsive reserve (YR

_{i}) services must also be optimally assigned. The relationship between the four operational parameters can be explained using Figure 1. From Figure 1, it is clear that the regulation up, regulation down, and responsive reserve capacities of the i

^{th}battery at hour t (YU

_{i}, YD

_{i}, and YR

_{i}) are governed by the POP for that hour and by the maximum charger power rate MP. The optimal assignment of these operational parameters for each battery at any hour makes the battery charging schedule and services bidding into the ancillary services markets optimal for the two options.

#### 2.1. EV Aggregator’s Objective Function

_{it}] is a function of the decision variables POP, YD, YU, and YR of that EV at hour t. It is also a function of the expected regulation up, regulation down, and responsive reserve capacities that will be dispatched at that hour, as shown in Equation (3). The calculation of ExU, ExD, and ExR is discussed in [13].

_{it}] at hour t (which indicates a charging process) by the forecasted energy price per kWh at hour t P

_{t}. The second part of the cost equation represents the EV battery degradation cost that is needed to be paid to the EV owners whenever their EV battery is discharged back to the grid by the EV aggregator when dispatching regulation and/or responsive reserve services. The degradation cost is calculated by multiplying a more conservative estimate of the negative expected power draw of the ith EV (which, indicates a discharging process) at hour t E[NFP

_{it}] by the battery discharging cost per kWh (DC

_{i}). E[NFP

_{it}] is considered as a more conservative estimation of the power draw because it only depends on the regulation up and responsive reserve services, as seen in Equation (5).

_{i}for any EV is given as follows:

_{it}] at hour t by the fixed charging tariff β.

_{t}). EVPer

_{t}is a function of the accumulated probability of the unexpected departure of all EVs at hour t, A_Dep

_{it}, which, in turn, is a function of the time of scheduled trips for each EV during the day (Equations (9) and (10)). In Equation (10), A_Dep is reset at the scheduled morning trip time MTrip and scheduled evening trip time ETrip because it is assumed that the availability of each EV at these two time slots is known with certainty.

#### 2.2. Optimization of EV Aggregator’s Operation

#### 2.3. Optimization of Dedicated Energy Storage Unit’s Operation

_{i}= 1 and EVPer = 1 for all t). Thus, the second part of Equation (6) needs to be omitted for the case of using energy storage units due to the fact that there is no profit making from charging the energy storage units. Thus, the expected daily income and cost equations become,

#### 2.4. Optimization of the Energy Storage Units’ Operation

## 3. Ancillary Services Algorithms

_{i}> M

_{ci}) or undercharging (SOC

_{i}< 0) for the battery. If overcharging or undercharging is to occur, the power draw PD

_{i}is not approved and needs to be adjusted. For the case of overcharging, the power draw must be limited to the remaining energy capacity of the battery CR. In the case of undercharging, the power draw PD

_{i}must be limited to the state of charge of the battery. The efficiency must be taken into account in both the charging and discharging cases. The algorithm takes the case of no regulation signal into account so that the power draw for that time span is simply the POP/res.

_{i}(f) from the regulation service is now subjected to another algorithm that calculates the power draw. The algorithm is shown in Figure 3 and is very similar to the previous algorithm. The algorithm shows that in the case of no dispatch signal of a responsive reserve service, the final power draw FP

_{i}of the battery at a certain time span is the resulting power draw from performing the regulation service. The third algorithm comes after the power draw calculation, after performing the ancillary services. It is used to update the state of charge according the resulting power draw. The algorithm is shown in Figure 4. The final power draw is checked so it does not cause overcharging or undercharging for the battery. If there is no occurrence of undercharging and overcharging, the final power draw is then checked to see whether it is positive (charging) or negative (discharging), so that the SOC update will take the effect of the efficiency into account. If the final power draw is causing overcharging or undercharging, the final SOC of the battery will either be the maximum (overcharging case) or equal to zero (undercharging case).

## 4. Case Study

## 5. Results and Discussion

#### 5.1. Charging Profiles

#### 5.2. Quarterly Results

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

i, t, d, w, y | Indices for EV, hour, day, week and year numbers |

SOCI | Battery’s initial state of charge |

SOC | State of charge |

Trip | Accumulated energy needed for scheduled trips |

POP | Preferred operating point |

MP, MC | Maximum power (kW) and energy ratings (kWh) |

YD, YU | Maximum/minimum additional power draw of EV |

time | Operational daily time span in hours. |

YR | Reduction in power available for spinning reserves |

ρ | Energy discharged due to discharge efficiency |

E[.] | Expected value |

T_{1}, T_{2} | Energy needed for scheduled morning/evening trip |

FP | Final power draw |

FP^{–-} | A conservative estimation of the final power draw |

Av | EV availability; 1 if EV is available, 0 otherwise |

CR | Charge remaining to be supplied to an EV |

EvPer | Expected % of EVs remaining to perform V2G |

NY, NW | Number of years and number of weeks |

Dep | Probability that an EV departs unexpectedly |

K_{w} | Weighting factor of week w |

A_Dep | Accumulated probability of unexpected departure |

Y_P | The operational profit of one year |

MTrip, ETrip | Scheduled morning and evening trip times |

Inv_C | investment cost |

In, C | Expected daily income and cost |

M_C | Smart meter cost for each EV |

P | Energy price |

Com_C | Communications cost for each EV |

PD, PU, PR | Forecasted price of regulation down, regulation up and responsive reserve. |

Bi_C | Retrofit cost to support bidirectional V2G |

RD, RU, RR | Aggregator’s capacity of regulation down, regulation up, and responsive reserve |

T_P | Project’s total profit |

β | Energy tariff charged to the customer |

Dis_rate | Discount rate |

ExD, ExU, ExR | Expected percentage of dispatched regulation down, regulation up, and responsive reserve |

P_P | Percentage of participation of EV owners in aggregation |

Deg | An epigraph variable to model battery degradation |

T_EV | Total available EVs in the targeted region |

DC | Discharging cost |

P_EV | Number of participating EVs in aggregation |

BatC | The battery replacement cost |

NEV | Number of EVs usage profiles |

Ef | Battery charging/discharging efficiency |

Ch_C | Battery charger cost ($/kW) |

Comp | Compensation factor for unplanned departures. |

E_C | Battery energy capacity cost ($/kWh) |

ISO | Independent system operator |

AS | Ancillary Services |

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**Figure 1.**Relationship between preferred operating point (POP) and ancillary services capacities of a certain battery.

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**MDPI and ACS Style**

Aldik, A.; Khatib, T.
EV Aggregators and Energy Storage Units Scheduling into Ancillary Services Markets: The Concept and Recommended Practice. *World Electr. Veh. J.* **2020**, *11*, 8.
https://doi.org/10.3390/wevj11010008

**AMA Style**

Aldik A, Khatib T.
EV Aggregators and Energy Storage Units Scheduling into Ancillary Services Markets: The Concept and Recommended Practice. *World Electric Vehicle Journal*. 2020; 11(1):8.
https://doi.org/10.3390/wevj11010008

**Chicago/Turabian Style**

Aldik, Abdelrahman, and Tamer Khatib.
2020. "EV Aggregators and Energy Storage Units Scheduling into Ancillary Services Markets: The Concept and Recommended Practice" *World Electric Vehicle Journal* 11, no. 1: 8.
https://doi.org/10.3390/wevj11010008