# 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 |

## References

- Hadley, S.W.; Tsvetkova, A.A. Potential Impacts of Plug-in Hybrid Electric Vehicles on Regional Power Generation. Electr. J.
**2009**, 22, 56–68. [Google Scholar] [CrossRef][Green Version] - International Energy Agency (IEA). The IEA World Energy Outlook 2011; IEA: Paris, France, 2011. [Google Scholar]
- Cao, Y.; Tang, S.; Li, C.; Zhang, P.; Tan, Y.; Zhang, Z.; Li, J. An Optimized EV Charging Model Considering TOU Price and SOC Curve. IEEE Trans. Smart Grid
**2012**, 3, 388–393. [Google Scholar] [CrossRef] - Das, R.; Thirugnanam, K.; Kumar, P.; Lavudiya, R.; Singh, M. Mathematical Modeling for Economic Evaluation of Electric Vehicle to Smart Grid Interaction. IEEE Trans. Smart Grid
**2014**, 5, 712–721. [Google Scholar] [CrossRef] - Stoeckl, G.; Witzmann, R.; Eckstein, J. Analyzing the capacity of low voltage grids for electric vehicles. In Proceedings of the 2011 IEEE Electrical Power and Energy Conference (EPEC), Winnipeg, MB, Canada, 3–5 October 2011; pp. 415–420. [Google Scholar]
- Kempton, W.; Letendre, S.E. Electric vehicles as a new power source for electric utilities. Transp. Res. Part Transp. Environ.
**1997**, 2, 157–175. [Google Scholar] [CrossRef] - Brooks, A.; Thesen, S. PG&E and Tesla Motors: Vehicle to Grid Demonstration and Evaluation Program. In Proceedings of the 23rd International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and Exhibition (EVS-23), Anaheim, CA, USA, 2–5 December 2007. [Google Scholar]
- Ota, Y.; Taniguchi, H.; Nakajima, T.; Liyanage, K.M.; Baba, J.; Yokoyama, A. Autonomous Distributed V2G (Vehicle-to-Grid) Satisfying Scheduled Charging. IEEE Trans. Smart Grid
**2012**, 3, 559–564. [Google Scholar] [CrossRef] - Guille, C.; Gross, G. A conceptual framework for the vehicle-to-grid (V2G) implementation. Energy Policy
**2009**, 37, 4379–4390. [Google Scholar] [CrossRef] - Escudero-Garzas, J.J.; Garcia-Armada, A.; Seco-Granados, G. Fair Design of Plug-in Electric Vehicles Aggregator for V2G Regulation. IEEE Trans. Veh. Technol.
**2012**, 61, 3406–3419. [Google Scholar] [CrossRef][Green Version] - Kempton, W.; Tomić, J.; Letendre, S.; Brooks, A. Vehicle-to-Grid Power: Battery, Hybrid, and Fuel Cell Vehicles as Resources for Distributed Electric Power in California. 2001. Available online: http://www.udel.edu/V2G/docs/V2G-Cal-2001.pdf (accessed on 3 May 2014).
- Brooks, A.; Gage, T. Integration of Electric Drive Vehicles with the Electric Power Grid—A New Value Stream. In Proceedings of the 18th International Electric Vehicle Symposium and Exhibition, Berlin, Germany, 20–24 October 2001. [Google Scholar]
- Sortomme, E.; El-Sharkawi, M.A. Optimal Charging Strategies for Unidirectional Vehicle-to-Grid. IEEE Trans. Smart Grid
**2011**, 2, 131–138. [Google Scholar] [CrossRef] - Sortomme, E.; El-Sharkawi, M.A. Optimal Combined Bidding of Vehicle-to-Grid Ancillary Services. IEEE Trans. Smart Grid
**2012**, 3, 70–79. [Google Scholar] [CrossRef] - Sortomme, E.; El-Sharkawi, M.A. Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services. IEEE Trans. Smart Grid
**2012**, 3, 351–359. [Google Scholar] [CrossRef] - Shafie-khah, M.; Heydarian-Forushani, E.; Golshan, M.E.H.; Siano, P.; Moghaddam, M.P.; Sheikh-El-Eslami, M.K.; Catalão, J.P.S. Optimal trading of plug-in electric vehicle aggregation agents in a market environment for sustainability. Appl. Energy
**2016**, 162, 601–612. [Google Scholar] [CrossRef] - Baringo, L.; Amaro, R.S. A stochastic robust optimization approach for the bidding strategy of an electric vehicle aggregator. Electr. Power Syst. Res.
**2017**, 146, 362–370. [Google Scholar] [CrossRef][Green Version] - Soares, J.; Ali, M.; Ghazvini, F.; Borges, N.; Vale, Z. Dynamic electricity pricing for electric vehicles using stochastic programming. Energy
**2017**, 122, 111–127. [Google Scholar] [CrossRef][Green Version] - Alipour, M.; Mohammadi-Ivatloo, B.; Moradi-Dalvand, M.; Zare, K. Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets. Energy
**2017**, 118, 1168–1179. [Google Scholar] [CrossRef] - Hoogvliet, T.W.; Litjens, G.B.M.A.; van Sark, W.G.J.H.M. Provision of regulating- and reserve power by electric vehicle owners in the Dutch market. Appl. Energy
**2017**, 190, 1008–1019. [Google Scholar] [CrossRef] - Aghajani, S.; Kalantar, M. Optimal scheduling of distributed energy resources in smart grids: A complementarity approach. Energy
**2017**, 141, 2135–2144. [Google Scholar] [CrossRef] - Mohammadi-Ivatloo, P.A.B.; Alipour, M.; Abapour, M.; Zare, K. Optimal scheduling of plug-in electric vehicles and renewable micro-grid in energy and reserve markets considering demand response program. J. Clean. Prod.
**2018**, 186, 293–303. [Google Scholar] - Moghaddam, S.Z.; Tohid, A. Network-constrained optimal bidding strategy of a plug-in electric vehicle aggregator: A stochastic/robust game theoretic approach. Energy
**2018**, 151, 478–489. [Google Scholar] [CrossRef] - Perez-Diaz, A.; Gerding, E.; McGroarty, F. Coordination and payment mechanisms for electric vehicle aggregators. Appl. Energy
**2018**, 212, 185–195. [Google Scholar] [CrossRef][Green Version] - Banshwar, A.; Sharma, N.K.; RajSood, Y.; Shrivastava, R. Market-based participation of energy storage scheme to support renewable energy sources for the procurement of energy and spinning reserve. Renew. Energy
**2019**, 135, 326–344. [Google Scholar] [CrossRef] - Aziz, M.; Oda, T.; Mitani, T.; Watanabe, Y.; Kashiwagi, T. Utilization of Electric Vehicles and Their Used Batteries for Peak-Load Shifting. Energies
**2015**, 8, 3720–3738. [Google Scholar] [CrossRef][Green Version] - Liu, H.; Ji, Y.; Zhuang, H.; Wu, H. Multi-Objective Dynamic Economic Dispatch of Microgrid Systems Including Vehicle-to-Grid. Energies
**2015**, 8, 4476–4495. [Google Scholar] [CrossRef] - Chen, Z.; Xiong, R.; Wang, K.; Jiao, B. Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm. Energies
**2015**, 8, 3661–3678. [Google Scholar] [CrossRef][Green Version] - Gao, Y.; Chen, Y.; Wang, C.-Y.; Liu, K.J.R. A contract-based approach for ancillary services in V2G networks: Optimality and learning. In Proceedings of the 2013 Proceedings IEEE INFOCOM, Turin, Italy, 14–19 April 2013; pp. 1151–1159. [Google Scholar]
- ERCOT. Electric Reliability Council of Texas, Market Information. Available online: http://www.ercot.com/mktinfo (accessed on 4 January 2018).
- Grant, M.C.; Boyd, S.P. Graph implementations for nonsmooth convex programs. In Recent Advances in Learning and Control; Blondel, V.D., Boyd, S.P., Kimura, H., Eds.; Springer: London, UK, 2008. [Google Scholar]
- Kempton, W.; Udo, V.; Huber, K.; Komara, K.; Letendre, S.; Baker, S.; Brunner, D.; Pearre, N. A Test of Vehicle-To-Grid (V2G) for Energy Storage and Frequency Regulation in the PJM System; University of Delaware: Newark, DE, USA, 2008. [Google Scholar]
- Nissan Leaf 2011 Specifications. Available online: https://www.nissanusa.com/digital-brochures/brochures/nissan.leaf.2011/pdf/nissan.leaf.2011.specs.pdf (accessed on 4 January 2018).
- TESLA. Tesla Motors Model S. Available online: http://www.teslamotors.com/models/features#/performance (accessed on 4 January 2018).
- MINI, MINI-E Specifications. Available online: http://www.mitsubishi-cars.co.uk/used-cars/i-miev/ (accessed on 4 January 2018).
- Federal Highway Administration. National Household Travel Survey, 2010. Available online: http://nhts.ornl.gov/ (accessed on 15 December 2018).

**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