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Transactions on Smart Grid

1

A Two-layer Energy Management System for

Microgrids with Hybrid Energy Storage considering

Degradation Costs

Chengquan Ju, Student Member, IEEE, Peng Wang, Member, IEEE,

Lalit Goel, Fellow, IEEE and Yan Xu, Member, IEEE

Abstract—The integration of renewable energy source (RES)

and energy storage systems (ESS) in microgrids has provided

potential beneﬁt to end users and system operators. However,

intermittent issues of RES and high cost of ESS need to be

placed under scrutiny for economic operation of microgrids.

This paper presents a two-layer predictive energy management

system (EMS) for microgrids with hybrid ESS consisting of

batteries and supercapacitors. Incorporating degradation costs

of the hybrid ESS with respect to the depth of charge (DOD)

and lifetime, long-term costs of batteries and supercapacitors are

modeled and transformed to short-term costs related to real-

time operation. In order to maintain high system robustness

at minimum operational cost, a hierarchical dispatch model is

proposed to determine the scheduling of utilities in microgrids

within a ﬁnite time horizon, in which the upper layer EMS

minimizes the total operational cost and the lower layer EMS

eliminates ﬂuctuations induced by forecast errors. Simulation

studies demonstrate that different types of energy storages can

be utilized at two control layers for multiple decision-making

objectives. Scenarios incorporating different pricing schemes,

prediction horizon lengths and forecast accuracies also prove the

effectiveness of the proposed EMS structure.

Index Terms—Optimization; microgrids; energy storage; en-

ergy management system (EMS); hierarchical control.

NOMENCLATURE

A. Indices and sufﬁxes

i Storage device index.

t Time index.

∆t Time interval.

t

u

, t

l

Time index in upper and lower layer.

∆t

u

, ∆t

l

Time interval in upper and lower layer.

B. State Variables

P

M

(t) Power of utility grid.

P

B

(t) Power of battery.

P

SC

(t) Power of supercapacitor.

P

L

(t) Power of load.

P

PV

(t) Power of PV.

P

WT

(t) Power of wind turbine.

This work was under the Adaptive Integrated Hybrid DC-AC Micro Power

Parks System (ERIP04) program with Singapore Economic Development

Board (EDB Singapore) and Energy Research Institute @ NTU (ERI@N).

The authors would like to thank ERI@N for the ﬁnancial support.

C. Parameters

DOD Depth of discharge of battery.

d

B

(∆t) Depth of Charge of battery in ∆t.

E

B.rat ed

Rated battery Capacity.

E

B

(t) Energy of battery at time t.

E

SC

(t) Energy of supercapacitor at time t.

η

Bc

, η

Bd

Charging and discharging efﬁciency of

battery.

η

SCc

, η

SC d

Charging and discharging efﬁciency of

supercapacitor.

a, b, c Curve-ﬁtting coefﬁcients of battery life-

time.

L

B

(d

B

) Battery lifetime with respect to DOD =

d

B

.

L

SC

Supercapacitor lifetime.

E

B A

(t) Actual capacity of battery at t.

E

a

(t) Accumulative energy of battery at t

C

B

Battery replacement cost.

C

B AC

Battery average degradation cost.

C

BDC

Battery degradation cost.

C

SC

Supercapacitor replacement cost.

C

SC DC

Supercapacitor degradation cost.

T

u

, T

l

Length of prediction horizon in upper

layer and lower layer.

P

min

M

(t), P

max

M

(t) Power limits of utility grid.

P

min

B

(t), P

max

B

(t) Power limits of battery.

P

min

SC

(t), P

max

SC

(t) Power limits of supercapacitor.

S

min

B

(t), S

max

B

(t) SOC limits of battery.

S

min

SC

(t), S

max

SC

(t) SOC limits of supercapacitor.

P

min

B

(t), P

max

B

(t) Power limits of battery.

σ

l

B

, σ

l

M

, σ

l

SC

Cost weighting coefﬁcients.

I. INTRODUCTION

I

N recent years, growing interest in renewable energy source

(RES) has prompted microgrids to develop towards more

intelligent and modernized entities. Microgrids integrate the

distributed generators including conventional and renewable

sources to supply predicted load of end-users in a decentralized

manner [1]. However, intermittency and undispatchability of

RES outputs induce system robust problems. Energy from

RES might be unavailable due to bad weather conditions when

electricity is needed [2]. Energy storage system (ESS) is usu-

ally integrated in microgrids to compensate power mismatch.

ESS can also act as bidirectional mediators with the utility