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Probabilistic Optimal Bi-level Scheduling of a Multi-Microgrid System with Electric Vehicles

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Abstract

In this paper, an efficient energy management system (EMS) is proposed for optimal operation of multiple electrically coupled microgrids (MGs). A new bi-level EMS is employed as an enhanced technique to coordinate vehicle-to-grid (V2G) operation of electric vehicles (EVs) with a stochastic framework in a multi-microgrid system. Hierarchical EMS helps the system to preserve the privacy of each MG. The EV scheduling and demand response programs have been integrated simultaneously in the optimization strategy to reduce the load demand of the peak hours and reshape the load profile. Uncertainties related to the system load demand, renewable generations, EV fleet behavior and energy price are considered. The proposed stochastic system is solved by adaptive particle swarm optimization algorithm. Numerical studies on a two electrically coupled industrial and residential MGs test system verify the efficiency of proposed EMS for cost reduction of the system and optimal operation of V2G.

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Abbreviations

APSO:

Adaptive particle swarm optimization

CEMS:

Central energy management system

DR:

Demand response

DRP:

Demand response program

EMS:

Energy management system

ELS:

Elitist learning strategy

EV:

Electric vehicle

ICA:

Imperialist competitive algorithm

FC:

Fuel cell

MG:

Microgrid

MMG:

Multi-microgrid

MT:

Microturbine

O&M:

Operation and maintenance

PDF:

Probability density function

PV:

Photovoltaics

SoC:

State of charge

V2G:

Vehicle to grid

WT:

Wind turbine

i, j :

Index for particles in PSO

d :

Index for dimensions in PSO

k :

Index for EV number

m, n :

Index for MG

s :

Index for generation sources

t, ta, tb :

Index for time

B :

Price bid of system components

Bold, Bnew :

Electricity price before/after DRP

c1, c2 :

Acceleration coefficients in PSO

C :

Cost of system components

C EV :

Cost of V2G program of EVs

C O&M :

Cost of O&M of microsources

dis i :

Mean distance of particle i in APSO

E V :

Energy of EV

E trip :

Energy consumed by EV in the trips

EiniV, EfinV :

Initial/final Energy of EV at the beginning and end of the day

E(ta, tb):

Self (ta = tb) or cross elasticity

f :

Evolutionary factor in APSO

pBest, gBest :

Personally/globally best particle in PSO

PLold, PLnew :

Demand value before/after DRP

∆PL :

Difference between demands before and after DRP

PG,WT, Pr,WT :

Generation/nominal power of WT

PG,PV, Pr,PV :

Generation/nominal power of PV module

P LC :

Power amount of load curtailment

P Gen :

Generation power of microsources

PCH, PDCH :

Charging/discharging power of EVs

PVIP, PVOP :

Virtual input/output power of MG

PMG,in, PMG,out :

Imported/exported power of the MG to/from other MGs

PGrid,in, PGrid,out :

Imported/exported power of the MG to/from the grid

P Load :

Total load of MG

RSTD, RC :

Standard/constant Solar radiation

R L :

Costumers’ revenue

S :

Customers’ benefit

U V :

State of EV in V2G program (charging/discharging/idle)

vr, vci, vco :

Nominal/cut-in/cut-out wind speed

v i d :

Velocity of particle i in PSO

w :

Inertia weight in PSO

x i d :

Position of particle i in PSO

ηCh, ηDCh :

Charging/discharging efficiency of EVs

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran

    Mohammad Mirzaei & Reza Keypour

  2. Electrical Engineering Section, Mads Clausen Institute, University of Southern Denmark, Odense, Denmark

    Mehdi Savaghebi

  3. Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

    Keyvan Golalipour

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  1. Mohammad Mirzaei
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  2. Reza Keypour
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  3. Mehdi Savaghebi
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  4. Keyvan Golalipour
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Correspondence to Keyvan Golalipour.

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Mirzaei, M., Keypour, R., Savaghebi, M. et al. Probabilistic Optimal Bi-level Scheduling of a Multi-Microgrid System with Electric Vehicles. J. Electr. Eng. Technol. 15, 2421–2436 (2020). https://doi.org/10.1007/s42835-020-00504-8

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  • DOI: https://doi.org/10.1007/s42835-020-00504-8

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