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Electric Vehicles in Energy Systems pp 289–312Cite as

Modelling the Impact of Uncontrolled Electric Vehicles Charging Demand on the Optimal Operation of Residential Energy Hubs

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Abstract

Development and implementation of energy hubs present a viable means of transitioning towards distributed and renewable energy generation and conversion technologies. Particularly in the residential sector, implementation of an energy hub infrastructure increases the resiliency of the local system against grid failures and provides economic and environment benefits with respect to system operation. However, recent transition towards electric mobility in the transportation sector introduces new challenges to the optimization of dispatch of energy vectors within residential energy hubs. This is partly due to the uncontrolled charging behavior exhibited by large plug-in electric vehicle (EV) fleets. Under uncontrolled charging conditions, residential energy hubs are impacted by significant additional energy consumption loads and must adapt appropriately to ensure optimal operation of the overall system. In this chapter, the impact of two levels of uncontrolled EV fleet charging rates are projected onto a residential energy hub under different distributed energy technology configurations. The effects of these various conditions on the optimal operation of the residential energy hub are evaluated using a mixed-integer linear programming approach. Economic and emission analysis of the results of this study indicate the operating cost-cutting potential of lower EV charging rates and co-generation technologies, as well as the corresponding tradeoff in emission generation. Furthermore, the operating cost and emission impacts of cogeneration of heat and power (CHP) implementation under an Ontario context were examined, indicating up to 34% operating cost reduction and a 49% emission increase resulting from CHP adoption. Finally, the limitations in solar photovoltaic adoption in the residential energy hub were discussed.

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Author information

Authors and Affiliations

  1. Department of Chemical Engineering, University of Waterloo, Waterloo, ON, Canada

    Azadeh Maroufmashat, Q. Kong, Ali Elkamel & Michael Fowler

  2. College of Engineering, Khalifa University of Science and Technology, The Petroleum Institute, Abu Dhabi, UAE

    Ali Elkamel

Authors
  1. Azadeh Maroufmashat
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  2. Q. Kong
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  3. Ali Elkamel
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  4. Michael Fowler
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Corresponding author

Correspondence to Azadeh Maroufmashat .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Bonab, Bonab, Iran

    Ali Ahmadian

  2. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Behnam Mohammadi-ivatloo

  3. University of Waterloo, Department of Chemical Engineering, College of Engineering, Khalifa University of Science and Technology, The Petroleum Institute Abu Dhabi, UAE, Waterloo, ON, Canada

    Ali Elkamel

Appendix A

Appendix A

The nomenclature is shown below.

Nomenclature

BESS

Battery energy storage system

CHP

Combined heat and power

DC

Direct current

DER

Distributed energy resource

GHG

Greenhouse gas

ESS

Energy storage system

MILP

Mixed integer linear programming

MILNP

Mixed integer non-linear programming

NHTS

National Household Travel Survey

EV

Electric vehicle

PV

Photovoltaic

SOC

State of charge

V2G

Vehicle-to-grid

Variables

εcharge, k

Charge efficiency for storage system k

εdischarge, k

Discharge efficiency for storage system k

εEV

Efficiency of EV charging

μ

Weight factor

Cij

Coupling matrix

Capbattery

Capacity of plug-in electric vehicle battery

Costfixed

Fixed cost of the energy technology systems

Costfuel(t)

Cost of fuels consumed in the energy hub during operation

Costoper, conv(t)

Operating cost of energy conversion systems

Costoper, stor(t)

Operating cost of energy storage systems

dtravelled

Distance travelled

\( \dot{E}(t) \)

Flow of energy into storage system

\( \dot{E_k}(t) \)

Flow of energy into storage system for energy vector k

Eloss(t)

Standby loss of energy from the storage system k

Emax, k

Maximum storage capacity of storage system k

EF

Emission factors associated with inflow energy vector set I(t)

I(t)

Inflow energy vector

Imin

Minimum flow capacities for the inflow energy set

Imax

Maximum flow capacities for the inflow energy set

i

Index for inflow energy vector set

j

Index for energy demand load set

k

Index for energy storage technologies

nPEV

Index for plug-in electric vehicle in fleet

ntotal

Total number of plug-in electric vehicles in fleet

O(t)

Energy demand load of the energy hub

Qcharge, k(t)

Power charged to storage system k

Qdischarge, k(t)

Power discharged to storage system k

QEV(t)

Charging required for the EV fleet

SoCk(t)

State of charge of the storage system k at timestep t

SoCk, min

Minimum charge capacity of the storage system k

SoCk, max

Maximum charge capacity of the storage system k

t

Index for time

tarrival

Time of arrival at energy hub

tdepart

Time of departure from energy hub

Z

Overall objective function

Z1

Cost objective function

Z2

Emissions objective function

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Maroufmashat, A., Kong, Q., Elkamel, A., Fowler, M. (2020). Modelling the Impact of Uncontrolled Electric Vehicles Charging Demand on the Optimal Operation of Residential Energy Hubs. In: Ahmadian, A., Mohammadi-ivatloo, B., Elkamel, A. (eds) Electric Vehicles in Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-34448-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-34448-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34447-4

  • Online ISBN: 978-3-030-34448-1

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