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Title: Research on Modeling and Hierarchical Scheduling of a Generalized Multi-Source Energy Storage System in an Integrated Energy Distribution System
Author: Weiliang Wang, Dan Wang, Liu Liu, Hongjie Jia, Yunqiang Zhi, Zhengji Meng and Wei Du

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energies
Article

Research on Modeling and Hierarchical Scheduling
of a Generalized Multi-Source Energy Storage System
in an Integrated Energy Distribution System
Weiliang Wang 1,2 , Dan Wang 1, *, Liu Liu 1, *, Hongjie Jia 1 , Yunqiang Zhi 1 , Zhengji Meng 1
and Wei Du 3
1

2
3

*

Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China;
wweiliang@tju.edu.cn (W.W.); hjjia@tju.edu.cn (H.J.); yuningzhi@tju.edu.cn (Y.Z.);
mzj941011@163.com (Z.M.)
State Grid Jiangsu Electric Power Company Maintenance Branch, Nanjing 210003, China
NARI Technology Co., Ltd., Nanjing 211106, China; duwei@sgepri.sgcc.com.cn
Correspondence: wangdantjuee@tju.edu.cn (D.W.); liulscu@tju.edu.cn (L.L.);
Tel.: +86-185-2263-6418 (D.W.); +86-133-0306-0197 (L.L.)

Received: 13 December 2018; Accepted: 5 January 2019; Published: 15 January 2019




Abstract: Energy storage systems play a crucial role in ensuring stable operation. However,
the development of system-level energy storage is hindered due to the restrictions of economy,
geography, and other factors. Transitions of traditional power systems into integrated energy
distribution systems (IEDS) have provided new solutions to the problems mentioned above. Through
intelligent control management methods, the utilization of multi-energy-type resources both on the
supply and demand sides shows the potential for equivalent storage characteristics. Inspired by the
aggregation principles, this paper aims at proposing a novel model named generalized multi-source
energy storage (GMSES), including the modeling and cooperation of three kinds of available resources:
conventional energy storage (CES), multi-energy flow resources (MFR), and demand response
resources (DRR). Compared with the conventional means of storage, GMSES can be regarded
as a more cost-effective and flexible participant in the proposed hierarchical energy scheduling
framework that can realize system-level storage services in IEDS. On this basis, a multi-timescale
energy scheduling strategy is proposed to reshape the regulation of IEDS operations and deal with
the fluctuations caused by renewable energy and loads, where the general parameter serialization
(GPS)-based control strategy is utilized to select and control the responsive loads in DRR. Furthermore,
a hierarchical scheduling algorithm is developed to generate the optimal set-points of GMSES.
Case studies are analyzed in an electricity-gas coupled IEDS. The simulation results show that the
coupled co-optimization GMSES model is conducive to achieving the goal of self-management and
economical operation, while the influence of the underlying IEDS on the upper energy system is
reduced, as the tie-line power fluctuations are smoothed out.
Keywords: generalized multi-source energy storage; hierarchical scheduling; multi-energy flow;
resource coordination

1. Introduction
Energy storage systems (ESSs) play important roles in improving economy [1], energy supply
stability [2], and energy efficiency [3]. Relative demonstrations around the world show that ESS is
conducive to promoting efficient energy consumption and renewable energy integration. In the U.S.,
ESS is highly supported and widely applied in different power utility companies [4]. In Europe, ESS is
regarded as one of the significant energy strategies in the Strategic Energy Technology (SET) Plan by
Energies 2019, 12, 246; doi:10.3390/en12020246

www.mdpi.com/journal/energies

Energies 2019, 12, 246

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Energies 2019, 11, x

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the European Commission, which facilitates the European Union’s efforts to achieve energy targets by
by and
the 2050
European
2020
[5]. Commission, which facilitates the European Union’s efforts to achieve energy
targets
by
2020
and operation
2050 [5]. mechanisms, ESSs can primarily be divided into electromagnetic,
Based on their
Based
on
their
operation
mechanisms,
ESSsascan
primarily
be divided
electromagnetic,
mechanical, electrochemical,
and
thermal ones,
shown
in Figure
1 [6]. into
Previous
studies have
mechanical, electrochemical, and thermal ones, as shown in Figure 1 [6]. Previous studies have shown
shown an urgent demand for system-level energy storage with low costs and high flexibility. However,
an urgent demand for system-level energy storage with low costs and high flexibility. However,
owing to the restrictions of physical properties and commercial factors, the conventional energy storage
owing to the restrictions of physical properties and commercial factors, the conventional energy
model needs further improvement for the following reasons: (1) the energy storage economics are
storage model needs further improvement for the following reasons: (1) the energy storage economics
of significant concern, both in the investment stage and the operation stage [7]; (2) the application of
are of significant concern, both in the investment stage and the operation stage [7]; (2) the application
ESS is limited by geography and external environment [8]; (3) durable system-level storage services
of ESS is limited by geography and external environment [8]; (3) durable system-level storage services
could
bebe
affected
andother
otherfactors
factors[9].
[9].Typical
Typical
ESS
storage
could
affectedby
byESS
ESScapacity,
capacity,cycle
cyclelife,
life, performance,
performance, and
ESS
storage
characteristics,
i.e.,
rated
characteristics,
i.e.,
ratedpower,
power,average
averagecapital
capitalcost,
cost,response
responsetime
timeand
andapplications,
applications, are
are classified
classified in
Figure
1
[6,7].
in Figure 1 [6,7].
Electromagnetic

Mechanical

Electrochemical

GMSES-DRR GMSES-CES
10

Capital Cost

GMSES-MFR

4

PHS
Spinning and
non-spinning reserve
CAES

Operation
scheduling

Seasonal
storage

102

10000+

3000

2500
Lead-acid

101
1

NaS

2000

VRB
Supercapacitor

1500

FES

Li-ion

10-1

STES

10-2
0

1000

Average Capital Cost ($/kWh)

Frequency
regulation

103

Rated Power (MW)

Thermal

500

Second

Minute

Hour

Day

0

Response Time
FES: flywheel energy storage VRB: vanadium redox battery
PHS: pumped hydro storage
CAES: compressed air energy storage
STES: seasonal thermal energy storage

Figure
1. Functional
applications
of energy
storage
systems
(ESSs)
and and
generalized
multi-source
energy
Figure
1. Functional
applications
of energy
storage
systems
(ESSs)
generalized
multi-source
storage
(GMSES)
[6,7].
energy storage (GMSES) [6,7].

Some
methods
have
been
proposed
to to
improve
thethe
existing
energy
storage
model.
InIn
reference
[10],
Some
methods
have
been
proposed
improve
existing
energy
storage
model.
reference
due
to
the
development
and
innovation
on
the
physical
materials
of
ESS,
the
energy
efficiency
[10], due to the development and innovation on the physical materials of ESS, the energy efficiency of
a vanadium
redox
flow
battery
was
improved
A dispatching
dispatchingmethod
method
hybrid
of a vanadium
redox
flow
battery
was
improvedfrom
from62%
62%to
to 76%.
76%. A
forfor
hybrid
ESS
was
minimizethe
the
cycle
in [11].
In reference
[12],heaters
waterand
heaters
and
ESS
wasdeveloped
developed to minimize
lifelife
cycle
costcost
in [11].
In reference
[12], water
battery
storage
systems
were were
coordinated
for optimum
demand
response,
controlled
by by
a proposed
battery
storage
systems
coordinated
for optimum
demand
response,
controlled
a proposed
mathematicalmodel.
model. These
to to
improve
the the
performance
and controllability
of ESS of
mathematical
These studies
studieswere
wereable
able
improve
performance
and controllability
to to
some
extent.
However,
controllable
resources
of multi-energy
networks,
demand side
and supply
ESS
some
extent.
However,
controllable
resources
of multi-energy
networks,
demand
side and
side, side,
possess
regulatable
potential
that that
hasn’t
beenbeen
coordinated
efficiently
in previous
studies.
supply
possess
regulatable
potential
hasn’t
coordinated
efficiently
in previous
studies.
Therefore,
the
energy
storage
model
still
faces
challenges
of
high
cost,
limited
power
output,
and
lack
Therefore, the energy storage model still faces challenges of high cost, limited power output, and
lack
of
all-around
applicability.
Even
though
investment
in
energy
storage
is
expected
to
decrease
in
thethe
of all-around applicability. Even though investment in energy storage is expected to decrease in
future,
the
mediancapital
capitalcost
costof
ofaabattery
battery system
system for
is is
still
predicted
to to
future,
the
median
foran
an8-h
8-hbattery,
battery,for
forexample,
example,
still
predicted
be more than 1000 $/kW [13].
be more than 1000 $/kW [13].

Energies 2019, 12, 246

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On the other hand, traditional power systems are undergoing transitions towards integrated
energy distribution systems (IEDS) [14], where the interdependencies and synergy effects among
various energy sectors are highlighted, including the electric power system (EPS), natural gas system
(NGS), and district heating system (DHS). The transitions have gained popularity in both academia
and industry [15]. In addition, controllable resources both on the supply and demand sides can be
regulated and integrated as flexible participants [16,17], which also provides new pathways to the
development of ESSs. In reference [18], home microgrids consisting of multiple distributed energy
resources are taken as players in the market, whose power is well dispatched with the hierarchical
bi-level controller. A bi-level model for long-term planning on wind investment is studied in [19],
where demand- side resources and wind turbines are coordinated. Considering the link function
between primary and secondary energy sources, electricity is taken as the core of IEDS. However,
other types of energy carriers are necessary in the following analysis, as they can provide energy
backup and enhance flexibility with respect to their complementary characteristics.
In this regard, this paper proposes a generalized multi-source energy storage (GMSES) model
and its hierarchical optimal scheduling strategy for operation and control. Three kinds of controllable
resources in GMSES are highlighted herein: conventional energy storage (CES), multi-energy flow
resources (MFR), and demand response resources (DRR); and energy carriers such as electricity,
gas, and heat are taken into consideration [20]. Interactions between electricity and other energy
carriers are feasible due to energy conversion technologies. As electricity shows a core position in
energy supply and unique characteristics in energy utilization, the GMSES power interaction mainly
refers to electricity in this paper. Direct application of other energy carriers will be discussed in
following papers.
The applications of advanced metering infrastructure (AMI) and information and communication
technology (ICT) motivate the development of energy scheduling on multiple time scales. Owing
to the uncertainties of the renewable energy and energy loads, there are power deviations between
the forecast results and real-time operation status [21], which may influence the energy scheduling
plan significantly. In this regard, day-ahead scheduling results need to be adjusted in response to
the updated energy forecast information. GMSES resources can be adopted for power regulation to
obtain stable operation. Therefore, three scheduling scenarios are addressed: day-head, intra-hour,
and ultra-short-term.
However, response characteristics of GMSES resources vary in multi-time scales. In this case,
a hierarchical framework may provide an appropriate way to solve this problem. A novel framework
is presented in [22] to study the impacts of investment incentives on generation expansion planning.
Based on a two-stage energy management strategy, wind power and plug-in electrical vehicles were
able to be used to minimize its operational cost and maintain the power balance in [23]. In reference [24],
with the hierarchical framework, the battery energy-storage system and renewable energy sources
could be combined to realize an economic generation schedule. Inspired by this, a hierarchical
scheduling framework for resource coordination is proposed for GMSES, where efficient energy
interactions are feasible between the system-level instructions and equipment response. Accordingly,
with the help of optimal scheduling planning and reasonable control strategies, integrated controllable
resources of GMSES can realize the flexible energy flow utilization and meet the requirements of
system-level storage service in IEDS.
The main contributions of this paper are summarized as follows.
(1)

(2)

By combining the resources of conventional energy storage, multi-energy flow and demand
response, a novel model named GMSES is proposed for a system-level equivalent energy
storage effect.
A hierarchical scheduling framework is studied to take advantage of complementary
characteristics of various resources in GMSES and meet the precise response to the control target.

Energies 2019, 12, 246

(3)

(4)

4 of 28

A coupled co-optimization model is developed for multi-type and multi-timescale coordinated
scheduling solution (including day-ahead, intra-hour and ultra-short-term scheduling),
promoting the economical and stable operation of IEDS.
A general parameter serialization (GPS)-based control strategy is adopted for the flexible
demand-side loads in GMSES.

The rest of this paper is organized as follows: Section 2 describes a functional framework and
the comprehensive modeling of GMSES. Section 3 presents a detailed description of the hierarchical
scheduling framework, as well as the multi-timescale optimization and control strategy of GMSES.
Verified by the case studies, Section 4 discusses the results for the economic scheduling and energy
balance service of GMSES. Finally, Section 5 draws the conclusions.
2. Functional Framework and Comprehensive Modeling of Generalized Multi-Source
Energy Storage
By coordinating the available resources of conventional energy storage, multi-energy flow and
demand response, GMSES is characterized by its advantage of combining high power density and high
energy density [25]. Complementary potential can be found in the coordination of GMSES controllable
resources, as it varies in time scales and operation characteristics, as shown in Figure 1. This paper
takes shorter scheduling scenarios of day-ahead, intra-hour and ultra-short-term as an example to
illustrate the details of GMSES resources.
(1)

(2)

(3)

CES mainly refers to traditional battery energy storage in this paper. Currently, some typical CESs
(i.e., lead-acid battery, lithium-ion battery, etc.) have been widely utilized for EPS applications [25].
To simplify the CES model, the lead-acid battery is selected with an operation time interval of
15 min to avoid too much power loss and lifecycle decrease caused by frequent dispatch [26].
MFR refers to the equivalent storage based on energy conversion and dispatch. Due to the
microturbine response characteristics, MFR can be applied for longer time-scale dispatch schemes
ranging from 15 min to 1 h in this paper, aiming at minimizing the operation costs in day-ahead
scheduling and smoothing out the fluctuations in intra-hour scheduling [27].
DRR refers to the equivalent storage that aggregates flexible demand-side loads with reasonable
control strategies [28]. Considering the fast-response characteristics of DRR, three typical
controllable loads, i.e., heat pump (HP), central air conditioning (CAC), and electric vehicle
(EV) are studied herein for energy balance service. The DRR operation time interval is set
as 1 min.

As the application scenario and available resources may change, other energy storage resources
like power-to-gas (P2G) [29] will be integrated in GMSES model in a subsequent study. Specifically,
due to the larger energy storage potential, P2G can be deployed to adjust high-level renewable energy
penetration [30]. Meanwhile, in this paper, considering the application scenario and energy scheduling
timescale, P2G systems are not included in the GMSES model.
In Figure 2, the day-ahead schedule plan is studied in Stage 1 based on the optimal energy flow
calculation, where the basic operation points of GMSES can be obtained. As the uncertainties vary
in multiple time scales, the fluctuations caused by energy demand and renewable energy need to be
smoothed out in the intra-hour schedule plan in Stage 2, and energy balance service is provided to
follow the real-time variations in the ultra-short-term schedule plan in Stage 3, as shown in Figure 2.
With flexible GMSES resources and the self-management energy scheduling strategy, the local impacts
on the upper level energy system can be minimized.

Energies 2019, 12, 246
Energies 2019, 11, x

5 of 28
5 of 29
Forecast error

Energy Forecast:
• Uncertainties
• Multi-time scale

Forecast value

Forecast error

Forecast value

Actual value

• Multi-energy flow
equations
• Static security
constraints
• End-use comfortconstraints
• Equipment
constraints

Renewable Energy
& Load Forecast
Errors

Forecast value

Actual value

Update of forecast data

Constraints

Forecast error

Forecast error

Actual value

Basic set-point reference

Stage 3: Ultra-short term
Energy Balance

Basic set-point reference



• Target: Ultra-short term
power fluctuation smoothing
• Dispatch time interval: 1 min
• GMSES resource: DRR

Stage 2: Intra-hour Scheduling

Update of forecast data

• Target: Power deviation
regulation
• Dispatch time interval: 15 mins
• GMSES resource: MFR + CES

Stage 1: Day-ahead Scheduling
• Target: Set the economic
operation plan and tie-line
power basic
• Dispatch time interval: 1 hour
• GMSES resource: MFR

Power deviation
regulation
Energy balance

Resource coordination

GMSES / Integrated Energy Distribution System Interface (electric power)
Energy conversion & dispatch

Multi-energy Flow Resource
(MFR): electricity, gas, heat

Optimal scheduling & control

Demand response & load control

Conventional Energy Storage
(CES): lead-acid battery

Demand Response Resource
(DRR): HP, EV, CAC

Generalized Multi-source Energy Storage (GMSES) Resource Pool

Figure2.2.Functional
Functionalframework
framework of generalized
generalized multi-source
(GMSES).
Figure
multi-sourceenergy
energystorage
storage
(GMSES).

2.1.2.1.
GMSES-Conventional
GMSES-ConventionalEnergy
EnergyStorage
Storage (CES)
(CES)
The
CES
storagecells.
cells.To
Toevaluate
evaluate
the
stored
energy
The
CESstores
storesenergy
energybased
basedon
on aa series
series of energy
energy storage
the
stored
energy
level,the
thestate
stateofofcharge
charge(SOC)
(SOC)isis studied
studied as
as aa significant
significant indicator.
as as
shown
in in
level,
indicator. ItItisismodeled
modeled
shown
Equations
(1)–(3),where
wherethe
theSOC
SOCtime
time continuity
continuity characteristic
overcharging
or or
Equations
(1)–(3),
characteristiccan
canbe
befound.
found.The
The
overcharging
overdischargingstatus
statusisisnot
notallowed
allowed in
in the
the normal
normal operation
thethe
SOC
overdischarging
operationofofGMSES-CES.
GMSES-CES.Therefore,
Therefore,
SOC
variations
are
limited
to
the
operation
constraints,
as
shown
in
Equations
(4)
and
(5).
In
addition,
variations are limited to the operation constraints, as shown in Equations (4) and (5). In addition, itit is
is assumed
the energy
of the
only
occurs
in the
charging
dischargingperiods,
periods,and
andthe
assumed
that that
the energy
lossloss
of the
CESCES
only
occurs
in the
charging
or or
discharging
the
SOC
at
the
initial
moment
should
be
equal
to
that
at
the
last
moment
in
an
operation
period,
as
SOC at the initial moment should be equal to that at the last moment in an operation period, as shown
in Equation
(6).
in shown
Equation
(6).
SSOC_k (t) − SSOC_k (t − ∆t) = ∆SSOC_k (t)
(1)
SSOC_k (t ) − SSOC_k ( t − Δt ) = ΔSSOC_k ( t )
(1)
CES
CES
∆SSOC_k = Pk,C
ηk,C ∆t/Wk,rated
(2)
CES
CES
(2)
ΔS k =CES
P ηk ,CΔt / W
k ,rated
CES
∆SSOC_kSOC_
= Pk,Dk ,C∆t/
(Wk,rated
ηk,D )
(3)
CES
(3)
ΔSSOC_k = PkCES
Δ
t
/
(
W
η
)
,D
k ,D
∆SSOC_k ≤ ∆SSOC_k
(t)k ,rated
≤ ∆S
(4)
SOC_k
ΔSSOC_≤
(t ) ≤ ΔSSOC_k
k ≤SΔSSOC_k (
SSOC_k
SOC_k t ) ≤ S
SOC_k

(4) (5)

SSOC_k (≤0S)SOC_
) ≤ SSOC_
SSOC_k
=k (St SOC_k
(T
k )

(5) (6)

SSOC_k (0) = SSOC_k (T)
2.2. GMSES Multi-Energy Flow Resource (MFR)

(6)

Energy conversion and dispatch technologies have promoted flexible energy coupling and
2.2.
GMSES Multi-Energy
Flowintegration
Resource (MFR)
multi-energy
complementary
in IEDS. Taking electricity as an example, with the help
of power
electronic
devices,
bi-directional
power flowhave
can be
realized flexible
if reasonable
strategy
Energy
conversion
and
dispatch technologies
promoted
energycontrol
coupling
and is
multi-energyThat
complementary
in IEDS.links
Taking
an example,
with
the help
of
implemented.
is to say, theintegration
energy-coupling
andelectricity
multipleas
energy
systems
coupled
thereto
power
electronic
devices,
bi-directional
power
flow
can
be
realized
if
reasonable
control
strategy
is
can be integrated and regulated as equivalent energy storage, which is defined as a multi-energy

Energies 2019, 11, x

6 of 29

implemented.
Energies
2019, 12, 246That is to say, the energy-coupling links and multiple energy systems coupled thereto
6 of 28
can be integrated and regulated as equivalent energy storage, which is defined as a multi-energy flow
resource (MFR) in the framework of GMSES. Therefore, the regulatable potential of multi-energy
flow
resource
(MFR)
in theutilized
framework
of GMSES.
Therefore,
the regulatable
potential
of multi-energy
networks
could
be fully
to support
the EPS.
An example
of GMSES-MFR
is shown
in Figure
networks
could be fully utilized to support the EPS. An example of GMSES-MFR is shown in Figure 3.
3.
Electric power system

Source
bus

Electric power system

Source
bus

GMSES-MFR

GMSES-MFR

PEC
AC

PEC
Charging/
standby status

CHP
Gas
source

BEH Type I

PEC

CHP

AC

GB

CHP

BEH Type II

Natural Gas
Electricity
Heat

Gas
source

PEC
Discharging
status

GB

BEH Type I

BEH Type II

Electricity Energy
Flow
Gas Energy Flow

Natural gas system

CHP

Natural gas system

(a)

(b)

Figure
3. 3.
Illustration
status,(b)
(b)discharging
discharging
status.
Figure
Illustrationofofthe
theGMSES-MFR:
GMSES-MFR:(a)
(a) charging/standby
charging/standby status,
status.

The
physical
of GMSES-MFR
GMSES-MFRis is
bi-directional
energy
hub (BEH),
its charging
The
physicalbasis
basis of
thethe
bi-directional
energy
hub (BEH),
and its and
charging
status
status
is defined
as when
electricity
into
theand
BEH,
the opposite
defines
the discharging
is defined
as when
electricity
flows flows
into the
BEH,
theand
opposite
defines the
discharging
status.
status.
Traditionally,
the energy
model
been
widely
used
multi-energy
flow
analysis.
Traditionally,
the energy
hub hub
(EH)(EH)
model
has has
been
widely
used
for for
multi-energy
flow
analysis.
However,
it is
not
abletotoexplain
explainbi-directional
bi-directional energy flow
EH
type
I inI in
However,
it is
not
able
flow characteristics.
characteristics.Taking
Takingthe
the
EH
type
Figure
3b
as
an
example,
when
the
electricity
generated
by
combined
heat
and
power
(CHP)
satisfies
Figure 3b as an example, when the electricity generated by combined heat and power (CHP) satisfies
demand-side
requirements,the
theexcess
excessparts
partscan
can be
be sent
sent back
Pe1Pnegative.
thethe
demand-side
requirements,
back to
tothe
theEPS,
EPS,which
whichmakes
makes
e1 negative.
At
this
time,
the
power
flows
into
the
air-conditioner
(AC)
system
are
negative,
as
the
dispatch
factor
At this time, the power flows into the air-conditioner (AC) system are negative, as the dispatch
factor
is
non-negative.
Obviously,
this
is
unreasonable
in
reality.
Hence,
the
BEH
model
is
studied
as
below,
is non-negative. Obviously, this is unreasonable in reality. Hence, the BEH model is studied as below,
where power electronic convertor (PEC) makes it possible for bi-directional power flow [31].
where power electronic convertor (PEC) makes it possible for bi-directional power flow [31].
To describe the behaviors of energy dispatch in GMSES-MFR, λC and λD of BEH are defined
To describe the behaviors of energy dispatch in GMSES-MFR, λC
and λD of BEH are defined as
as dispatch factors in the charging/standby and discharging status. Specifically,
taking BEH type I as
dispatch
factors
in
the
charging/standby
and
discharging
status.
Specifically,
BEH
an example, when MFR is in the charging status, the load demand is satisfied bytaking
both EPS
andtype
NGSI as
an while
example,
when
MFR
is
in
the
charging
status,
the
load
demand
is
satisfied
by
both
EPS
and
NGS
Pe1 is positive. If the ratio of Lh1 to Le1 is equal to the heat-power ratio of CHP, the NGS can
while
Pe1 both
is positive.
If and
the thermal
ratio of loads,
Lh1 towhich
Le1 is means
equal that
to the
heat-powerisratio
CHP, the
NGS
supply
electrical
GMSES-MFR
in theofstandby
status
andcan
supply
whichthe
means
that GMSES-MFR
is in the
standby
and
Pe1 isboth
zero.electrical
When theand
gasthermal
input Pg1loads,
increases,
electricity
generated by CHP
could
be sentstatus
back to
P
the
EPS
through
the
PEC,
which
means
that
the
MFR
is
discharging
and
is
negative.
BEH
type
Pe1 is zero. When the gas input Pg1 increases, the electricity generated by e1CHP could be sent backI to
charging/standby
andwhich
discharging
canMFR
be modeled
using Equations
and (8). BEH type I in
theinEPS
through the PEC,
meansstatus
that the
is discharging
and Pe1 is(7)
negative.
e
P
charging/standby and discharging status
modeled
using
Equations
(7)
and
(8).



1− 

 Le1can
 be
e1
C1
CHP
(7)
 P 
L  = 
AC
h
CHP   g1#"
"
#  h1" C1
#

e
Le1
1 − λC1 ηCHP
Pe1
e
(7)
  Pe1 
D1 ) h CHP
 Le1  = −1 (1 −AC
Lh1  =  λC1
η
η
P
(8)
CHPh   g1
AC
 Lh1 

 −D1

(1 − D1 ) CHP   Pg1 

"
# "
#"
#
e
Similarly, BEH typeLe1II in charging/standby
and
status
−1/(1 − λD1
) discharging
ηCHP
Pe1 could be modeled in
=
(8)
h
Equations (9) and (10). L
−λD1 η AC /(1 − λD1 ) ηCHP
Pg1
h1
 Le2 

1

e
C2CHP

  Pe2 

(9) in
Similarly, BEH type II in charging/standby
and discharging
status could be modeled
 
L  = 
GB
h
 h2  0 (1 − C2 ) + C2CHP   Pg2 
Equations (9) and (10).

"

Le2
Lh2

#

"

=

1
0

e
λC2 ηCHP
GB
h
(1 − λC2 )η + λC2 ηCHP

#"

Pe2
Pg2

#
(9)

Energies 2019, 12, 246

7 of 28

"

Le2
Lh2

#

"

=

e
−1
λD2 ηCHP
h
0 (1 − λD2 )η GB + λD2 ηCHP

#"

Pe2
Pg2

#
(10)

The energy conversion and dispatch processes can be obtained via energy coupling component.
Therefore, the output power boundaries of the BEH can be illustrated as
(

lb = L − Pe,max + klb
Pe1
e1
CHP
ub = L + Pe,max /η AC + kub
Pe1
e1
AC

Type I

(
Type II

lb = L − Pe,max + klb
Pe2
e2
CHP
ub = L + kub
Pe2
e2

(11)

(12)

where k represents the correction factor of the effect of energy sectors on the output of Pe . It should be
pointed out that the method of multi-energy flow analysis could also be applied to other scenarios in
the IEDS.
The participation of NGS has broadened the border of energy regulation in GMSES, where energy
flow analysis is adopted for the coupling relationship consideration. The mass-flow balance equation
of NGS can be calculated in Equation (13). Furthermore, the interfaces between BEH and NGS mainly
refer to the BEH consumed gas power Pg1 and Pg2 , which influence the gas demand ωl,i and ωl,j at
the connected node i and node j in Equation (14) and are constrained by the gas pressure level in
Equation (15). As the IEDS discussed in this paper involves low-pressure scenarios, Lacey’s equation
is used to express the relation between gas flow and pressure drop, as shown in Equation (16) [32].
The natural gas flow calculation could be solved using the Newton–Raphson method, the details for
which can be found in [32].
ANGS Qr + ωs − ωl = 0
(13)
"
# "
# " P
#
without
with
g1
ωl,i
ωl,i
GHV
(14)
=
+
Pg2
with
without
ωl,j
ωl,j
GHV

∆pr = −0.5 ×

Sg v2g f r Lr
Dp
s

Qr = 5.72 × 10−4

[

(∆pr ) Dp5
]
f r Lr S g

(15)

(16)

2.3. GMSES Demand Response Resource (DRR)
When applied to ultra-short-term scheduling, the energy storage capacities required, as well
as the investment costs and the power losses of GMSES-CES, will increase significantly, while the
GMSES-MFR is not suitable for frequent adjustment due to the microturbines’ inherent response
characteristics [33]. Therefore, to accommodate the forecast errors flexibly and economically, DRR with
fast-response characteristics is utilized to manage the energy consumption of different load groups [33].
With the corresponding control strategy, DRR could be implemented to provide the required power
increase or decrease, which is similar to the energy storage effect. The DRR considered herein primarily
refers to the typical power-controllable flexible loads, including EV and temperature-controlled loads
such as HP and CAC, which are widely used in the household, industry and commerce.
2.3.1. General Model of GMSES-DRR
The key operation parameters of DRRs have common characteristics such as being controllable,
sequenceable, and combinable [12], and they can be expressed by general parameters to construct
a general model of GMSES-DRR [34]. The operation characteristics with respect to key operation
parameters and consumed power of DRRs are shown in Figure 4, where the power consumptions

Energies 2019, 12, 246

8 of 28

Energies 2019, 11, x

8 of 29

of
CAC
have
a corresponding
thermoelectric
coupling
relationship
with
the location’s
HPHP
andand
CAC
have
a corresponding
thermoelectric
coupling
relationship
with the
location’s
room
room
temperature,
as
shown
in
Figure
4a,b,
respectively.
In
contrast
to
the
HP
temperature
temperature, as shown in Figure 4a,b, respectively. In contrast to the HP temperature curve,curve,
the
the
charging
state
energy
status
of determine
EV determine
its power
consumption,
as shown
in Figure
charging
state
andand
energy
status
of EV
its power
consumption,
as shown
in Figure
4c. 4c.
Indoor temperature(℃)
Upper
boundary

Charging trajectory
Actual charging boundary
Actual charging trajectory
Nominal charging track

Indoor temperature(℃)

Upper
boundary1

Heating
mode

Upper
boundary2

Non-heating
mode

Charging state
Idle
mode Operation
Gear deadband mode
deadband
adjustment

Idle state

Lower
boundary2
Lower
boundary

Time

Time

Lower
boundary1

Time

Power consumption

Power consumption

Upper
boundary

Energy status

lower
boundary

Load rate
regulation

Time
Power consumption
Time

Time

Time
(a) Operation characteristic of a single HP

(c) Operation characteristic of a single EV

(b) Operation characteristic of a single CAC

Figure 4.
4. Operation
Operationcharacteristics
characteristicsof
ofGMSES-DRR.
GMSES-DRR.
Figure

In
characteristicsof
ofdifferent
differentDRRs,
DRRs,the
thegeneral
generalmodel
modelofofcontrollable
controllable
In addition
addition to
to the
the operation
operation characteristics
load
groups
could
be
further
described
by
the
dynamic
mechanism
and
physical-power
coupling
load groups could be further described by the dynamic mechanism and physical-power coupling
model
dynamic mechanism
mechanism is
is proposed
proposedin
inEquation
Equation(17).
(17).
model [28].
[28]. A
A typical
typical DRR
DRR dynamic
E m( E=1( E,m1E,2E m2, ,...,
E mh h,..., E mH ) H
Em n =
mn
mn . . . , Emn , . . . , Emn )
n

n

n

n

(17)
(17)

n

Taking the operation status Ehmh as an example, its dynamic mechanism can be described by
Taking the operation status Em
as an example, its dynamic mechanism can be described by
n
Equation (18).
Equation (18).
RE mt +Δ,ht = f (TP1 ,..., TPo ,..., TPO , Q1t ,..., Q at ,..., Q At , F1t ,..., Fbt ,..., FBt )
(18)
t+∆t
t
t
t
t
t
t
REmn ,hpower
= f ( TP
, . . . , is
TPdetermined
. . ,own
Q a , . operation
. . , QA , F1 , control
. . . , Fb , .variables
. . , FB ) and rated
(18)
o , . . . , TPO , Q
1 , .its
The operation
of 1DRR
by
n

n

power, while the operation control variables are determined by the load’s key operation parameters
The operation power of DRR is determined by its own operation control variables and rated
and operation conditions in consecutive periods. In general, the operation power consumption of
power,
while
the operation
control variables
by the load’sTaking
key operation
DRR has
a certain
correspondence
with the are
keydetermined
operation parameters.
DRR in parameters
operation
and
operation
conditions
in
consecutive
periods.
In
general,
the
operation
power
consumption
of DRR
h
status
as
an
example,
the
physical-power
coupling
model
formed
by
the
key
operation
Em
h
has
a
certain
correspondence
with
the
key
operation
parameters.
Taking
DRR
in
operation
status
Em
parameters can be illustrated by Equations (19) and (20).
n
as an example, the physical-power coupling
model
formed
by the key operation parameters can be
h
h
h
h
Pm = r ( Fm , Pm ,rated ,η m )
(19)
illustrated by Equations (19) and (20).
n

n

n

 U
= V
 Fmt ,h
 n

n

n

Qt ≤ Qt
Qa ≥ Qa, +

h
h a
h
Pmh n =
,a,t η− m
)
mn , Pmn ,rated
t + Δtr ( F
t
n

Fmn ,h

(19)
(20)

else

Qta ≤ Qta,−

U
Based on the physical-power coupling
model,
the
consumed power of DRR could be controlled
∆t
Fmt+n ,h
= V
(20)
Qta ≥ Qta,+

and regulated by changing the physical parameters.
The model parameters and coupling variables

 Ft
mn ,h 1. else
of the above three kinds of DRR are shown in Table
Based on the physical-power coupling model, the consumed power of DRR could be controlled
Table 1. Model parameters and coupling variables of DRRs.
and regulated by changing the physical parameters. The model parameters and coupling variables of
the
above three kinds ofTPDRR
are shown in Table 1. Qat
Fbt
U, V
Name
o
HP

thermodynamic parameters
of related buildings

indoor temperature

EV

energy state

energy state
energy state boundaries

on/off
state
charging
state

close-0
open-1
idle-0
charge-1

Energies 2019, 12, 246

9 of 28

Table 1. Model parameters and coupling variables of DRRs.
Name

TPo

Qta

Ftb

U,V

HP

thermodynamic parameters of
related buildings

indoor temperature

on/off state

close-0
open-1

EV

energy state

energy state
energy state boundaries

charging state

idle-0
charge-1

CAC

thermodynamic parameters of
related buildings

indoor temperature

load rate

heating mode
non-heating mode

2019,Control
11, x
2.3.2.Energies
General
Strategy of GMSES-DRR

9 of 29

By meansthermodynamic
of the general parameter
responsive
parameters serialization (GPS)-based control strategy,
heating
mode groups
CAC
indoor temperature
load rate
of related
buildings
non-heating
modeto the
on the demand side
and priority
sequence of GMSES-DRR will be determined
to respond
scheduling target [33–35]. The implementation of the GPS is introduced as follows:
2.3.2.1—Load
General Control
Strategy ofAccording
GMSES-DRR
Step
group division.
to the operation status, DRR can be divided into several
load groups
in a control
period.parameter
The loadserialization
group division
Jtm forcontrol
the type
m load
of DRR is
shown in
By means
of the general
(GPS)-based
strategy,
responsive
groups
on the
demand
sequence
of GMSES-DRR
Equation
(21),
whereside
K isand
thepriority
total number
of load
groups. will be determined to respond to the
scheduling target [33–35]. The implementation of the GPS is introduced as follows:
t

t

t

t

, . .the
. , Joperation
) DRR can be divided into several (21)
m = ( Jm,1 to
Step 1—Load group division. JAccording
status,
m,k , . . . , Jm,K
t
load groups in a control period. The load group division J m for the type m load of DRR is shown in
t by
Step
2—General
serialization
index
integration.
Determine the general serialization index bbm
Equation
(21), where
K is the total
number
of load groups.
n
t
t
integrating key operation parameters, asJillustrated
Equation
(22).
= ( J t , , J by
,
,Jt )
m ,1

m

m ,k

(21)

m ,K

t
t Determine the
t general serialization index bb t
Step 2—General serialization
integration.
m
bbm
= gindex
( Q1t (m
n ), . . . , Q a ( mn ), . . . , QA ( mn ))
n
by integrating key operation parameters, as illustrated by Equation (22).

n

(22)

t
Step 3—Responsive group selection.
the
general
bbmt = g (QBased
Q at ( m
, Q At ( m n )) serialization index bbt , the
(22)load is
1 ( m n ),  ,on
n ),
t
t
arranged in ascending or descending order in Figure 5, where bbmn ,max and bbmn ,min
represent the
Step 3—Responsive group tselection. Based on the general serialization
index bbt , the load is
t of ordered responsive load groups
upperarranged
and lower
boundaries
of
bb
.
The
consumed
power
DL
t
t
mn
in ascending or descending
order in Figure 5, wherem bbm ,max and bbm ,min represent the
can be
expressed
as follows:
upper
and lower
boundaries of bbmt . The consumed power DLtm of ordered responsive load groups
se,1
se,x
se,k
, . . . , dlm
, . . . , dlm
)
(23)
can be expressed as follows: DLtm = (dlm
n

n

n

n

t

se ,1

se , x

se , k

se,x
DLm = (of
dlmthe
,,xth
dlm load
,, dlmin) the kth selected load group. (23)
where dlm
represents the consumed power
Step
4—Responsive
load
determination.
According
to kth
the selected
load response
target, responsive
where
dlmse, x represents
thenumber
consumed
power of the xth
load in the
load group.
t
Step 4—Responsive
load number
determination.
to the load
load numbers
eu of the kth load
group are
determined,According
and the selected
loadresponse
units aretarget,
shown in
responsive
load numbers eu t of the kth load group are determined, and the selected load units are
Figure
5.
)
( t
shown in Figure 5.
eu



t
min ∑ euDLtmt ( x ) − tPm,tar
(24)



min
(24)
x =1 DLm ( x ) − Pm ,tar 
t



x =1



Step 5—Responsive load control. Regulate the operation control variables of each load in the
Step 5—Responsive load control. Regulate the operation control variables of each load in the
selected
eut responsive
loads.
TheThe
GPS
proposed
ininthis
illustratedasasinin
Figure
selected
loads.
GPS
proposed
thispaper
paper can
can be
be illustrated
Figure
6. 6.
eu t responsive
General serialization index
Descending
order

Selected load

O

Operate

Ascending
order

Unselected load
Close

Figure
5. Controlled
logic
diagram
ofofthe
serialization
(GPS)-based
control
strategy.
Figure
5. Controlled
logic
diagram
thegeneral
general parameter
parameter serialization
(GPS)-based
control
strategy.

Energies 2019, 12, 246

10 of 28

Energies 2019, 11, x

10 of 29

Upload the operation control variables

of load groups

Load group division based on operation status using Eq.(19)

Load group

Load group

Identification of key
parameters

Measure &
update

Identification of key
parameters

Load group

Identification of key
parameters

Integration of key parameters using Eq.(20)

Control &
regulate

Response unit selection using Eq.(21) and (22)
Regulate operation control variables of each selected load
Heat pump
control variables

Central air conditioning
control variables

Electric vehicle
control variables

Figure
Figure 6. Flowchart
Flowchart of
of the
the general
general parameter
parameter serialization (GPS)-based control strategy.
strategy.

2.4. Virtual
Virtual State
State of
of Charge
Charge of
2.4.
of GMSES
GMSES
Similar to
named virtual state of charge (VSOC)
VSGMSES
Similar
to the
the SOC
SOC of
of conventional
conventional ESS,
ESS, an
an index
index VS
GMSES named virtual state of charge (VSOC)
is
defined
to
evaluate
the
remaining
capacity
of
GMSES.
Operation
of GMSES
is defined to evaluate the remaining capacity of GMSES. Operation performance
performance of
GMSES can
can be
be
reflected via
can
bebe
prevented,
which
is
reflected
via VSOC,
VSOC,and
andin
inthis
thisregard,
regard,GMSES
GMSESovercharge
overchargeororoverdischarge
overdischarge
can
prevented,
which
vital
for
Consideringthe
thevarious
various
characteristics
of GMSES
resources,
is vital
forsafe
safeand
andstable
stable operation. Considering
characteristics
of GMSES
resources,
the
the integrated
VSGMSES
is defined
based
power
output
and
theintegrated
integratedweight
weightfactor
factor of
of each
VSGMSES
integrated
is defined
based
on on
thethe
power
output
and
the
part of GMSES, as illustrated in Equations
Equations (25)
(25) and
and (26).
(26).
VS GMSES ( t ) = λ MFRVS MFR ( t ) + λ CES S CES ( t ) + λ DRRVS DRR ( t )

VSGMSES (t) = λMFR VSMFR (t) + λCES SCES (t) + λDRR VSDRR (t)
e _ res ( t )
| PP
e_res ( t )|
res =
λresλ=
Pe _ res((tt )|
)
| Pe_res
∑
res

= MFR,
res=
DRR
res
MFR,CES,
CES,
DRR

(25)
(25)
(26)
(26)

res

VSMFR ,, SSCES and
VSDRR represent
where VS
and VS
representthe
the state
state of
of charge
charge of
of MFR,
res ,, PPe _ res ((tt))
where
MFR, CES
CES and
and DRR;
DRR; and
and λλres
e_res
MFR CES
DRR
represent
the
integrated
weight
factor
and
the
consumed
power
of
each
kind
of
GMSES
resources.
represent the integrated weight factor and the consumed power of each kind of GMSES resources.
As
GMSES is
built on
on the
the concept
concept of
of resource
resource aggregation,
aggregation, the
definition of
VSOC may
may be
be
As GMSES
is built
the definition
of VSOC
specialized when it comes to different scenarios. For example, the VSOC of GMSES-MFR can be
specialized when it comes to different scenarios. For example, the VSOC of GMSES-MFR can be
calculated using Equation (27). As for GMSES-DRR, the VSOC is defined based on the specific
calculated using Equation (27). As for GMSES-DRR, the VSOC is defined based on the specific
consumed demand-side power and its weights. The VSOC for the type m load of DRR in node σ
consumed demand-side power and its weights. The VSOC for the type m load of DRR in node σ can
can be calculated by Equation (28), while the DRR weight factor ϖ m is illustrated in Equation (29).
be calculated by Equation (28), while the DRR weight factor vm is illustrated in Equation (29).
VSMFR (t ) =

VSMFR (t) =
M

Pe (t ) − Pe min (t )
− PPe emin
PP
t )−
e max
min (t )( t )
e ( t()

Pemax (t) −
m Pemin ( t )

VS DRR (t ) =  (
Mm

Pm (t ) − Pdown (t )
ϖ m)
m
Pupm (t ) − Pdown
m(t )

Pm (t) − Pdown (t)
VSDRR (t) = ∑ ( m
vm )
m
P
upP(mt()t )− Pdown ( t )
m
ϖm =

vm =

M

PmP ((tt))
m

m

M

(27)
(27)
(28)
(28)

(29)
(29)

Pm (t)boundaries of the consumed power Pe (t ) ;
where Pe max (t ) , Pe min (t ) represent the upper and∑lower
m
m
Pm ( t ) represents the responded power of the type m load of DRR; and Pupm ( t ) , Pdown
(t ) represent the
upper and lower boundaries of Pm ( t ) .

Energies 2019, 12, 246

11 of 28

where Pemax (t), Pemin (t) represent the upper and lower boundaries of the consumed power Pe (t);
m ( t ), P m
Pm (t) represents the responded power of the type m load of DRR; and Pup
down ( t ) represent the
upper
and lower boundaries of Pm (t).
Energies 2019, 11, x
11 of 29
3. Hierarchical Optimal Scheduling with Generalized Multi-Source Energy Storage
3. Hierarchical Optimal Scheduling with Generalized Multi-Source Energy Storage
3.1. Framework of Optimal Scheduling with Generalized Multi-Source Energy Storage
3.1. Framework of Optimal Scheduling with Generalized Multi-Source Energy Storage
To take full advantages of the GMSES controllable resources and provide the precise response to
To take full advantages of the GMSES controllable resources and provide the precise response
the
target,
this paper
proposes
a hierarchical
scheduling
framework
of GMSES,
as shown
toscheduling
the scheduling
target,
this paper
proposes
a hierarchical
scheduling
framework
of GMSES,
as
inshown
Figurein7.Figure
The7.framework
is
divided
into
three
layers:
a
system
layer,
an
aggregation
layer,
The framework is divided into three layers: a system layer, an aggregation layer,
and
an
equipment
layer.
and an equipment layer.

GMSES Control
Center

Natural
Gas Setcor

Power
Sector

Heat
Secotr

GMSES-DRR
GMSES-CES
GMSES-MFR

Information
Flow

Information
Flow

Natural Gas
Setcor

Information
Flow

Heat
Secotr

Power
Sector

Load
group

Load
group

MFR
Controller

Storage
Controller

System Layer

Load
Aggregator

Aggregation Layer
Information
feedback

Control
signal

Information
Flow
Information
feedback

Control
signal

Storage
Fleet
Load
Group

Natural Gas
Electricity

Heat

BEH Group

Equipment Layer

Figure 7. Hierarchical
ofof
GMSES.
Figure
Hierarchicalscheduling
schedulingframework
framework
GMSES.

Thesystem
system
layer
is primarily
responsible
for collecting
the energy
information
(1)(1) The
layer
is primarily
responsible
for collecting
the energy
forecastforecast
information
(including
(includingheat
electricity,
heat and
natural
gas)
and the operation
information
of the parts
GMSES
parts
electricity,
and natural
gas)
and the
operation
information
of the GMSES
(including
(including
CES,
MFR
and
DRR).
According
to
the
current
system
operation
conditions,
thesets
CES, MFR and DRR). According to the current system operation conditions, the system layer
system
layer
sets
the
optimal
scheduling
plan
and
transmit
the
information
to
GMSES
the optimal scheduling plan and transmit the information to GMSES subsystems.
subsystems.
(2) The
aggregation layer can be regarded as a nexus that is responsible for converting the upper-layer
(2) The aggregation layer can be regarded as a nexus that is responsible for converting the upperoptimal scheduling plan into the corresponding control signals, such as the BEH dispatch factors
layer optimal scheduling plan into the corresponding control signals, such as the BEH dispatch
of MFR and energy storage unit instructions. As the core of energy conversion, the MFR controller
factors of MFR and energy storage unit instructions. As the core of energy conversion, the MFR
controller is abstracted mathematically based on the energy station. In addition, information
feedback and equipment aggregation of the equipment layer are available. Thus, equipment
constraints can be obtained and specified.

Energies 2019, 12, 246

(3)

12 of 28

is abstracted mathematically based on the energy station. In addition, information feedback and
equipment aggregation of the equipment layer are available. Thus, equipment constraints can be
obtained and specified.
The equipment layer mainly refers to the groups of controllable units. They upload their own
operation information to the aggregation layer. Simultaneously, upper-layer control signals
can be received. Therefore, the operation status of the controllable units is regulated for the
implementation of the scheduling plan.

Hence, this framework can realize the coupling of the information flow and energy flow of
GMSES. Compared with the methods of centralized control and distributed control, each layer in
this hierarchical scheduling framework can adopt suitable regulation methods based on the specific
operation characteristics of GMSES controllable resources, which indicates a high degree of extensibility.
Furthermore, information redundancy and complicated communications caused by centralized control,
as well as the multi-stakeholder consensus paradox in distributed control, can be avoided to some
extent [28].
3.2. Hierarchical Optimal Scheduling
3.2.1. Objectives
1.

Optimal objective for day-ahead scheduling

To minimize the IEDS operation costs, GMSES-MFR is selected and regulated, which also provides
basic tie-line power setting points for further correction, as shown in Equations (30) and (31).
minCcost =

T

T

t =1

t =1

∑ (CEPS,t + CNGS,t + CBEH,t ) = ∑ (πe−buy,t PEPS,t + πg,t PNGS,t + CBEH,t )
CBEH,t =

2.

πe−buyt − πe−sell,t
πe−buy,t + πe−sell,t
Pe,t +
| Pe,t | + πg,t Pg,t
2
2

∀t ∈ T (30)

(31)

Optimal objective for intra-hour scheduling

In the intra-hour scheduling, GMSES-MFR and GMSES-CES are called to track the basic set-points
of day-ahead scheduling. As the pipeline linepack in NGS can be regarded as short-term gas storage,
natural gas fluctuations can be stabilized to some extent [36]. Thus, the electric power fluctuations in
the IEDS are focused on in this paper. The objective of following the electric power setting points is set
as follows.
( 0
)
T

2
set
min ∑ Pex,t − Pex,t
∀ t ∈ T0
(32)
t

3.

Optimal objective for ultra-short-term scheduling

Owing to the fast response service required, GMSES-DRR is called on to maintain the energy
balance and handle aperiodic power fluctuations. This objective can be expressed in Equation (33).
Additionally, in order to control and determine the proportion of different types of controllable loads
in GMSES-DRR, the regulation cost minimization is defined as a further optimal DRR objective in
Equation (34).
( 00
)
T

set 2
min ∑ Pex,t − Pex,t
∀t ∈ T00
(33)
t00



M


t
min( ∑ Cm βtm Pmt−1 − Pm,tar
)

∀t ∈ T00

(34)

m =1

where βtm represents the parameter evaluating the degree of DRR participation in optimal regulation
and control, as shown in Equation (35).

Energies 2019, 12, 246

13 of 28

βtm =

t
t
Pm,up
− Pm,tar
t
t
Pm,up − Pm,down
t
t
 Pm,tar − Pm,down





+ ε m,down

Load reduction



+ ε m,up

Load increase

t
t
Pm,up
− Pm,down

(35)

where ε m,up , ε m,down represent the predefined and small fixed values to guarantee a positive βtm ,
which indicates the proportion of DRR participating in the power regulation; Cm ε m,down , Cm ε m,up
represent the user-baseline compensation costs given by the IEDS when the residents are willing to
participate in the demand response program. It can be found that a DRR with smaller βtm is more
suitable for power regulation, as βtm is relevant to the remaining regulation capacity.
3.2.2. Constraints
The constraints of the day-ahead optimization problem mainly include the multi-energy flow
constraints (as shown in Equations (36)–(38)), the BEH constraints (as shown in Equations (7)–(12)),
and the output power boundary constraints (as shown in Equation (39)).


 0 = F(xEPS , xNGS , xBEH )
0 = G(xEPS , xNGS , xBEH )

 0 = BEH(x , x
EPS NGS , xBEH )

(36)

xEPS_min < xEPS < xEPS_max

(37)

xNGS_min < xNGS < xNGS_max

(38)

xBEH_min < xBEH < xBEH_max , xBEH ∈ {PEC, AC, CHP, GB}

(39)

where F, G, BEH represent the multi-energy flow equations of EPS, NGS, and BEH. In addition,
an unbalanced three-phase power flow model is adopted to illustrate the EPS operation characteristics,
while the Lacey’s equation is integrated to analyze the relationship between gas pressure and gas flow
rate in NGS. A decomposed solution to the IEDS multi-energy flow calculation is adopted, details of
which can be found in [32].
Owing to the response characteristics of CHP, AC, and gas boiler (GB) in BEH, GMSES-MFR can
also be applied to intra-hour optimal scheduling [37]. The constraints are the same as those in the
day-ahead scheduling problem. When GMSES-CES is integrated to the intra-hour optimal scheduling,
the relative constraints can be seen in Equations (1)–(6).
When it comes to the ultra-short-term time scale, the effective aggregation of GMSES-DRR is
utilized due to the fast response characteristic. The operation boundaries of GMSES-DRR are shown in
Equations (40) and (41). To be specific, Equation (40) illustrates the regulation boundary constraints of
the total DRR groups at node σ in upper-layer optimization, while Equation (41) aims at the type m
load of DRR group at node σ.
M

N

M

N

mn ,t
t
mn ,t
≤ PD,σ
≤ ∑ ∑ Pup,σ
∑ ∑ Pdown,σ
m n =1

(

t
t
Pm,down
≤ Pm,tar
≤ Pmt−1

Load reduction

Pmt−1

Load increase



t
Pm,tar

(40)

m n =1



t
Pm,up

(41)

3.3. Hierarchical Optimal Scheduling Algorithm
Hierarchical optimal scheduling study is achieved based on the IEDS-GMSES co-simulation
platform, which is a combination of the open distribution system simulator (OpenDSS) [38] and
Matlab, as shown in Figure A1 in the Appendix A. A modified particle swarm optimization (PSO)

Energies 2019, 12, 246

14 of 28

algorithm [39] is adopted in searching the optimal operation points of GMSES. In this regard, the
hierarchical
scheduling solution can be divided into the following steps, as shown in Figure14
8. of 29
Energies 2019, 11, x
Main Algorithm
Start

Day-ahead optimal scheduling

• BEH dispatch factors
• BEH interactive power
• BEH consumed gas
• NGS node pressure

• EPS energy flow
• NGS energy flow
• Capacity constraints
• Output power boundaries

Control
instruction

Power
adjustment

Intra-hour optimal scheduling

Ultra-short-term energy balance

Objective:suppress intra-hour power deviation

Objective:smooth ultra-short-term power fluctuation

Operation constraints
• Multi-energy flow
• Capacity constraints
• Output power boundaries

and
constraints

Decision variables
• HP consumed power
• CAC consumed power
• EV consumed power

Operation
points

Update
operation plan

Operation constraints
• HP room temperature
• CAC room temperature
• EV energy state


Operation
constraints

GMSES comprehensive modeling
Day-ahead optimal
scheduling
• GMSES-MFR

Ultra-short-term
energy balance
• GMSES-DRR

Intra-hour optimal
schduling
• GMSES-MFR + CES

Equipment
layer

Decision variables
• BEH dispatch factors
• BEH interactive power
• BEH consumed gas

and
of the CES

Aggregation layer

Data Input
Data forecasts & collect
• Forecast data of renewable
energy & energy load
• Energy price
• Initialization data

System layer

Objective:minimize energy cost of the IEDS
Decision variables
Operation constraints

Solver

Solver

Optimal Scheduling Solution
PSO optimal algorithm ( Matlab + OpenDSS )

Update
operation points

Optimal dispatch model
• Optimal objective
• Operation constraints and penalty
function handle


Set PSO parameters
• Flying range of decision variables
• Population size, generations
• Boundaries of particle velocity


N
Is convergence
condition satisfied?

Y

Solve the hierarchical optimal scheduling problem of IEDS based on GMSES
Output global optimal result, generate IEDS optimization scheduling scheme
End

Figure8.8.Flowchart
Flowchartof
ofhierarchical
hierarchical optimal
optimal scheduling
Figure
schedulingalgorithm.
algorithm.

Step
1: Obtain
forecast
ofrenewable
the renewable
energy,
and energy
price.
Step
1: Obtain
the the
forecast
datadata
of the
energy,
energyenergy
loads, loads,
and energy
price. Initialize
Initialize
the
IEDS
data
and
the
PSO
algorithm.
the IEDS data and the PSO algorithm.
Step
Basedononthe
theenergy
energyconversion
conversion and
and dispatch
dispatch of
Step
2: 2:Based
of GMSES-MFR
GMSES-MFRmodel,
model,the
theday-ahead
day-ahead
optimal scheduling is formulated to optimize the multi-energy flow of the IEDS.
optimal scheduling is formulated to optimize the multi-energy flow of the IEDS.
Step 3: According to the updated data and upper-layer signal, the GMSES resources are
Step 3: According to the updated data and upper-layer signal, the GMSES resources are aggregated
aggregated in the aggregation layer for the intra-hour optimal scheduling and ultra-short-term
in the
aggregation
for the
and
energy
balance.
energy
balance. Inlayer
this way,
theintra-hour
stable and optimal
economicscheduling
operation of
theultra-short-term
IEDS can be ensured,
while
the
In tie-line
this way,
the
stable
and
economic
operation
of
the
IEDS
can
be
ensured,
while
the
tie-line
power
power deviation can be regulated. Subsequently, the optimal operation points are generated
deviation
be regulated.
the optimal operation points are generated and sent to the
and sentcan
to the
equipment Subsequently,
layer.
equipment
Steplayer.
4: The equipment layer responds to the optimal operation points and the available
controllable
unitsequipment
are regulated.
Theresponds
PSO algorithm
studied operation
to solve thepoints
hierarchical
scheduling
Step 4: The
layer
to theisoptimal
and the
available
problem above.
theregulated.
convergence
is satisfied,
the global
optimal
is outputted,
and
controllable
units If
are
Thecondition
PSO algorithm
is studied
to solve
theresult
hierarchical
scheduling
the
IEDS
optimal
scheduling
plan
is
generated.
Otherwise,
the
operation
points
need
to
be
updated
problem above. If the convergence condition is satisfied, the global optimal result is outputted, and
and go back to step 2.
In step 4, the flowchart of the DRR control strategy algorithm can be implemented in Figure 9,
where multiple load groups are managed generally by the general parameter serialization (GPS)-

Energies 2019, 12, 246

15 of 28

the IEDS optimal scheduling plan is generated. Otherwise, the operation points need to be updated
and go back to step 2.
In step 4, the flowchart of the DRR control strategy algorithm can be implemented in Figure 9,
Energies 2019, 11, x
15 of 29
where
multiple load groups are managed generally by the general parameter serialization (GPS)-based
control strategy. Firstly, according to the ultra-short-term objective of smoothing the power fluctuation,
based control strategy. Firstly, according to the ultra-short-term objective of smoothing the power
regulation PDt of DRR is obtained.
Secondly, based on the optimization of DRR regulation cost
fluctuation, regulation P t of DRR is obtained. Secondly, based on the optimization
of DRR
t
minimization, the modifiedD regulation target for each type of controllable loads Pm,tar
can be calculated.
regulation cost minimization, the modified regulation target for each type of controllable loads Pmt ,tar
Subsequently, considering the operation state limits of each type of DRR, the GPS is adopted for
can be calculated. Subsequently, considering the operation state limits of each type of DRR, the GPS
the
responsive
loadresponsive
group control...The
operation
parameters
areparameters
selected and
as
is adopted
for the
load groupkey
control.
The key
operation
areintegrated
selected and
.. the detailed procedure with regard to further
serialization
parameters
using
Equation
(22)
[34],
and
integrated as serialization parameters using Equation (22) [34], and the detailed procedure with
priority
and control
of responsive
loads
is mainly loads
described
in Section
2.3.2.
In this
regard tosequence
further priority
sequence
and control
of responsive
is mainly
described
in Section
regard,
GMSES-DRR
is
regulated
to
achieve
an
equivalent
energy
storage
effect
in
response
to the
2.3.2. In this regard, GMSES-DRR is regulated to achieve an equivalent energy storage effect
in
control
targets.
response to the control targets.
Calculate the regulated power in each node of IEDS

Regulation cost minimization
Upper and lower
boundaries of DRR
groups in each node

Determine the regulated power of specific units
Upper and lower
boundaries of
each DRR

Determine DRR control strategy
General serialization
indexes

General parameter serialization-based control strategy (GPS) of GMSES-DRR
Indoor Temperature (oC)

Indoor Temperature (oC)

6
5

Energy State
6

6

7

7

7

8
5

9

8

5

9

10
11
4

10

2

14

11

15
16

Indoor
temperature in
ascending order

17
18

2
19
20
1

1

O

3

Heat

Nonheat

CAC

Indoor
temperature in
descending order

13

13
14
3

15
16

Indoor
temperature in
ascending order

Open

Close

14

Energy state in
descending order

15
16
17

17
18

2
19

Energy state in
ascending order

18

19
20

20
1

1

Time O

12

4

12

13
3

9

11

4

12

Indoor
temperature in
descending order

8

10

1

Time O Charge

HP

1

Idle

Time

EV

Regulate operation control variables of response groups

Figure 9. Flowchart of DRR control strategy algorithm.

4.
Case Study
Study
4. Case
A
is selected
for case
verification
of theof
proposed
hierarchical
scheduling
strategy,
A local
localcity
cityregion
region
is selected
for case
verification
the proposed
hierarchical
scheduling
which
haswhich
less impact
onimpact
the connected
main grid,
as shown
inshown
Figure in
10.Figure
The IEDS
herein
includes
strategy,
has less
on the connected
main
grid, as
10. The
IEDS
herein
the
EPS
(an
IEEE37-node
power
distribution
system),
NGS
(an
11-node
low-pressure
gas
distribution
includes the EPS (an IEEE37-node power distribution system), NGS (an 11-node low-pressure gas
system),
and system),
GMSES (CES,
MFR, DRR).
regional
loads
be classified
distribution
and GMSES
(CES,The
MFR,
DRR).
Thecan
regional
loads into
can conventional
be classified ones
into
and
coupled
ones.
The
conventional
loads
primarily
refer
to
the
industrial
loads
supplied
by EPS
or
conventional ones and coupled ones. The conventional loads primarily refer to the industrial
loads
NGS
directly,
where
the
energy
demand
is
relatively
stable.
The
coupled
loads
refer
to
the
residential
supplied by EPS or NGS directly, where the energy demand is relatively stable. The coupled loads
and
loads with
multiple
and flexible
demand,and
like electricity,
gas and
heat. like
refercommercial
to the residential
and
commercial
loads energy
with multiple
flexible energy
demand,

electricity, gas and heat.

Energies 2019, 12, 246

16 of 28

Energies 2019, 11, x
Energies 2019, 11, x

16 of 29
16 of 29
Gas
Source
Gas
Source

GMSES-DRR
GMSES-DRR

725

GMSES-MFR

706

725

706

720

707

720

707

704
GMSES-DRR

704

GMSES-DRR

714

718

714

718

N11

Source Bus
Source Bus

N11
799

713

799

713

GMSES-MFR

N9

N7

N9

N7

729

744
727
703

701

702

703

727 730
730

GMSES-CES
GMSES-CES
GMSES-DRR

GMSES-DRR

GMSES-DRR

GMSES-DRR

GMSES-MFR
GMSES-MFR

N3

N6

N3

N6

N8

744

702

705

N5
N5

728

701

742

N2
N2

722

N10

712

N4

N4
728

729

712

724

724

722

N10

GMSES-CES

N8

731

GMSES-CES

731
709

741

775

709

741

775

732

708

732

708

733

734

737

738

733
735

734

737

738

735

705

742

Coupled Node
Coupled Node
Compressor
& gas load
Compressor
&
gas load
Substation
Transformer
Substation
Transformer
Load
Transformer
Load
Transformer

711
711
740

710

736

710

736

740

Figure10.
10. Topology
Topology of
of IEDS
IEDS based
Figure
basedon
onGMSES.
GMSES.
Figure 10. Topology of IEDS based on GMSES.

The
GMSESisisadopted
adoptedasasfunctional
functional resources
resources for the
The
GMSES
the IEDS
IEDS energy
energyscheduling.
scheduling.GMSES-MFR
GMSES-MFR
The
GMSES
is
adopted
as
functional
resources
for
the
IEDS
energy
scheduling.
consists
BEH
typeI and
I andBEH
BEHtype
typeII,
II,which
which are
are connected
inGMSES-MFR
thethe
EPS
and
consists
of of
BEH
type
connected to
tonode
node728
728and
andnode
node724
724
in
EPS
and
consists
of
BEH
type
I
and
BEH
type
II,
which
are
connected
to
node
728
and
node
724
in
the
EPS
and
node
N3
and
node
N4
in
the
NGS.
The
BEH
load
data
is
shown
in
Figure
A2.
GMSES-CES
is
node N3 and node N4 in the NGS. The BEH load data is shown in Figure A2. GMSES-CES is connected
node N3 and
node731
N4 in the
NGS.
ThetheBEH
data
is shown
in Figureloads,
A2. GMSES-CES
is
to node
node
732Four
in
EPS.load
Four
groups
of controllable
including
HP,
to connected
node 731 and
node 732and
in the
EPS.
groups
of controllable
loads, including
HP,
CAC and
EV
connected
to make
node 731
andGMSES-DRR,
node 732 in the
EPS.
Four
groups
of controllable
loads,
including
HP,
CAC
and
EV
up
the
which
are
located
in
node
712,
713,
720
and
735
in
the
EPS,
make up the GMSES-DRR, which are located in node 712, 713, 720 and 735 in the EPS, as shown in
CAC
and EV
make up
GMSES-DRR,
which are DRRs
located
inshown
node 712,
713, 720
and
the EPS,
as shown
in Figure
11. the
The
detailed of
parameters
are
in Tables
A1[34,35,40],
and735
A2in[34,35,40],
Figure
11. The
detailed
parameters
DRRs areofshown
in Tables
A1
and A2
where the
as
shown
in
Figure
11.
The
detailed
parameters
of
DRRs
are
shown
in
Tables
A1
and
A2
[34,35,40],
where the regulation costs of DRR in this paper are taken from [34]. In addition, the wind power and
regulation costs of DRR in this paper are taken from [34]. In addition, the wind power and photovoltaic
where
the regulation
costs ofinto
DRRIEDS,
in this
paper
takendata
fromis[34].
In addition,
the wind
power and
photovoltaic
are integrated
and
the are
forecast
illustrated
in Figure
A3 [41,42].
The
arephotovoltaic
integrated into
IEDS,
and
the
forecast
data
is
illustrated
in
Figure
A3
[41,42].
The
energy
prices
into [42],
IEDS,
the in
forecast
is illustrated
in Figure
A3 are
[41,42].
The
energy pricesare
are integrated
obtained from
asand
shown
Figure data
A4. Three
scheduling
scenarios
studied,
areenergy
obtained
from
as shown
in Figure
A4. in
Three
scheduling
scenarios are
studied,
brief
prices
are[42],
obtained
from
as shown
Figure
A4.sake
Three
scenarios
arewhose
studied,
whose brief
descriptions
can
be[42],
found
in Table
2.
For the
ofscheduling
stable operation
of GMSES,
the
descriptions
can
be
found
in
Table
2.
For
the
sake
of
stable
operation
of
GMSES,
the
upper
and
lower
whose
brief
descriptions
can be(including
found in Table
For the sakeare
of set
stable
operation
of respectively.
GMSES, the
upper and
lower
limits of VSOC
SOC of2.GMSES-CES)
to 15%
and 85%,
limits
of and
VSOC
(including
GMSES-CES)
set to 15% and
upper
lower
limits of SOC
VSOCof(including
SOC are
of GMSES-CES)
are 85%,
set torespectively.
15% and 85%, respectively.
Store
energy
Store
energy
Power
exchange
Power
exchange
Release
energy
Release
energy

Energy
flow
Energy
flow

Information Energy
flow
flow
Energy
Information
flow
flow

Node 712
Node 712

Information Energy
flow
flow
Energy
Information
flow
flow

Information Energy
flow
flow
Energy
Information
flow
flow

Node 720
Node 713
Demand
Side713
Controllable Resource
Aggregation
Node
720
Node
Demand Side Controllable Resource Aggregation

Figure 11. Illustration of the GMSES-DRR.
Illustration of
Figure11.
11. Illustration
Figure
of the
the GMSES-DRR.
GMSES-DRR.
Table 2. Brief description of case study.
Table 2. Brief description of case study.

Scenarios
Scenarios
Case 1
Case 1
Case 2
Case 2

Descriptions
Descriptions
In the day-ahead scenario, the multi-energy flow can be
In
the day-ahead
scenario,
the multi-energy
optimized
for the sake
of economical
operation.flow can be
optimized
for
the
sake
of
economical
operation.
In the intra-hour scenario, with the updated forecast data,
In
the intra-hour
with the updated
forecast
power
deviation scenario,
in the day-ahead
scheduling
candata,
be
power
deviation in the day-ahead scheduling can be
regulated.
regulated.

Information
flow
Information
flow

Node 735
Node 735

Responsive
Responsive
Resources
Resources
GMSES-MFR
GMSES-MFR

Time
Time
Scale
Scale
1h
1h

GMSES-MFR
GMSES-MFR
GMSES-CES
GMSES-CES

15
15
min
min

Energies 2019, 12, 246

17 of 28

Table 2. Brief description of case study.
Scenarios
Case 1
Energies 2019, 11, x
Case 2

Responsive Resources

Time Scale

In the day-ahead scenario, the multi-energy flow
can be optimized for the sake of
economical operation.

Descriptions

GMSES-MFR

1h

In the intra-hour scenario, with the updated
forecast data, power deviation in the day-ahead
scheduling can be regulated.

GMSES-MFR
GMSES-CES

15 min

17 of 29

In the ultra-short-term scenario, energy balance service can
17 of 29
Case 3
be provided
and the aperiodic power fluctuations caused by
GMSES-DRR
1 min
service can be provided and the aperiodic power
GMSES-DRR
1 min
Case 3
renewable
energy
and
variation
can
be smoothed.
fluctuations
caused
by load
renewable
energy
andbalance
load
In
the ultra-short-term
scenario,
energy
service can
Energies 2019, 11, xIn the ultra-short-term scenario, energy balance

variation
can and
be smoothed.
be
provided
the aperiodic power fluctuations caused by
renewable
energy
and load variation can be smoothed.
4.1. Case 1: Day-Ahead
Optimal
Scheduling

Case 3

GMSES-DRR

1 min

4.1. Case 1: Day-Ahead Optimal Scheduling
In
day-ahead
optimal
scheduling
4.1.the
Case
1: Day-Ahead
Optimal
Scheduling scenario, the GMSES-MFR resource is utilized to minimize
In theoperation
day-ahead
optimal
scheduling
scenario, control
the GMSES-MFR
resource
is utilized to
minimize
the IEDS
costs
through
the reasonable
of the BEH.
The decoupled
scenario
is
In the day-ahead
optimal scheduling
scenario,
the GMSES-MFR
resource
is utilized
to minimize
the IEDSfor
operation
costs that
through
control
of the
BEH.
The
decoupled
scenario
is the
studied
studied
comparison,
is to the
say,reasonable
in the coupled
nodes,
the
electric
loads
are supplied
by
EPS
the IEDS operation costs through the reasonable control of the BEH. The decoupled scenario is
for comparison,
is to say,
in the
nodes,
the electric
loads
by the EPSthat
directly,
directly,
and thethat
thermal
loads
arecoupled
supplied
by the
NGS via
GB.are
It supplied
can be concluded
the
studied for comparison, that is to say, in the coupled nodes, the electric loads are supplied by the EPS
and the thermal
loadsimproves
are supplied
by the
NGS via GB.
It
can be concluded
that
thedecreased
application
of
application
of
GMSES
the
IEDS
economics.
The
operation
costs
of
the
IEDS
from
directly, and the thermal loads are supplied by the NGS via GB. It can be concluded that the
GMSES
improves
the
IEDS
economics.
The
operation
costs
of
the
IEDS
decreased
from
$6699.21
to
$6699.21
to
$6285.37,
as
shown
in
Figure
12.
Furthermore,
the
BEH
operation
costs
indicate
a
29.62%
application of GMSES improves the IEDS economics. The operation costs of the IEDS decreased from
$6285.37,
as
shown
in
Figure
12.
Furthermore,
the
BEH
operation
costs
indicate
a
29.62%
reduction
reduction
from
to shown
$983.16.
regulating
the dispatch
factors,
the difference
in energy
prices
$6699.21
to $1397.01
$6285.37, as
in By
Figure
12. Furthermore,
the BEH
operation
costs indicate
a 29.62%
from
$1397.01
to $983.16.
Bytoregulating
dispatch
factors,
difference
in
energy
reflected,
is
reflected,
which
shows
the
flexible
supply
in
IEDS.the
The
results
BEH
regulation
are
shown
reduction
from
$1397.01
$983.16.energy
Bythe
regulating
the
dispatch
factors,
the of
difference
in prices
energy is
prices
is shows
reflected,
shows
the flexible
supply
IEDS. The
results
of BEH regulation
arein
shown
which
flexible
energy
supplyenergy
in IEDS.
Theinresults
of BEH
regulation
are shown
Figure 13.
in
Figure
13. thewhich
in Figure 13.

Figure 12. Results of day-ahead optimal scheduling.
Figure
12. Results
Results of
of day-ahead
day-ahead optimal
optimal scheduling.
scheduling.
Figure
12.

Figure
Illustration of
of BEH
BEH regulation
in in
Case
1. 1.
Figure
13.13.Illustration
regulationresults
results
Case

Coupled
units
such
CHP
and
theBEH
BEH
are the
basis
for
flow
Coupled
units
such
asasCHP
andGB
GBinin
the
BEH
arephysical
the
physical
basis
for the multi-energy
Figure
13. Illustration
of
regulation
results
in Case
1.the multi-energy
regulation.
The
essence
of
this
is
that
the
complementary
characteristics
and
synergy
effects
of
multiflow regulation. The essence of this is that the complementary characteristics and synergy effects of
energy flow
are are
adopted.
On the
other
BEH
canare
be regarded
thebasis
interface
of
coupling,
Coupled
units
such
as CHP
and
GBhand,
in the
BEH
the
physical
for
theenergy
multi-energy
flow
multi-energy
flow
adopted.
On
the
other
hand,
BEH
can
beasregarded
as the
interface
of energy
and the optimal results of GMSES-MFR resources indicate the support of coupled energy sector. The
regulation. The essence of this is that the complementary characteristics and synergy effects of multiexcess power can be sent back to EPS when the demands of BEH are satisfied. That is to say, energy
energy flow are adopted. On the other hand, BEH can be regarded as the interface of energy coupling,
can be stored through different energy forms. The BEH consumption of natural gas is shown in Figure
and the
13d.optimal results of GMSES-MFR resources indicate the support of coupled energy sector. The
excess power
can beofsent
back to EPS
when
the demands
ofcharge
BEH are
to say,the
energy
The VSOC
GMSES-MFR
could
evaluate
the state of
andsatisfied.
response That
effect is
towards
can becontrol
storedsignal.
through
energy
forms.
The BEH
consumption
of line
natural
gas is14)
shown
Figure
Thedifferent
VSMFR1 is at
its highest
between
20:00
and 22:00 (blue
in Figure
due tointhe

Energies 2019, 12, 246

18 of 28

GMSES-MFR VSOC

coupling, and the optimal results of GMSES-MFR resources indicate the support of coupled energy
sector. The excess power can be sent back to EPS when the demands of BEH are satisfied. That is to say,
energy can be stored through different energy forms. The BEH consumption of natural gas is shown in
Figure 13d.
The VSOC of GMSES-MFR could evaluate the state of charge and response effect towards the
control signal. The VSMFR1 is at its highest between 20:00 and 22:00 (blue line in Figure 14) due to the
Energies 2019, 11, x
18 of 29
Energies 2019,
11,charging
x
18 of time,
29
relatively
high
power of BEH type I, as shown in Figure 13c. However, at the same
theVS
VS
is its
at lowest
its lowest
in Figure
14);isthis
is because
the output
BEH
II is
MFR2
MFR2
is at
(red(red
line line
in Figure
14); this
because
the output
power power
of BEH of
type
II istype
closer
VSMFR2 is at its lowest (red line in Figure 14); this is because the output power of BEH type II is closer
closer
tolower
its lower
boundary.
to its
boundary.
to its lower boundary.
1

VS

VS

MFR1

MFR2

VSOC lower boundary

VSOC upper boundary

0.8
0.6
0.4
0.2
0

0

5

10

15

20

25

t(h)
Figure14.
14.Results
Resultsof
of VSOC
VSOC of GMSES-MFR.
Figure
GMSES-MFR.
Figure 14. Results of VSOC of GMSES-MFR.

4.2.4.2.
Case
2: 2:
Intra-Hour
Case
Intra-HourOptimal
OptimalScheduling
Scheduling
4.2. Case 2: Intra-Hour Optimal Scheduling
With
thethe
utilization
ofofavailable
and GMSES-CES,
GMSES-CES,intra-hour
intra-hour
optimal
With
utilization
availableresources
resourcesof
ofGMSES-MFR
GMSES-MFR and
optimal
With the utilization of available resources of GMSES-MFR and GMSES-CES, intra-hour optimal
scheduling
is
therefore
analyzed
to
track
the
tie-line
power
variation
in
the
day-ahead
scenario
scheduling is therefore analyzed to track the tie-line power variation in the day-ahead scenario (Case
scheduling is therefore analyzed to track the tie-line power variation in the day-ahead scenario (Case
(Case
1). The
to analyzing
liesthe
in regulatory
the regulatory
methods
in response
the control
target.
1). The
keykey
to analyzing
this this
issueissue
lies in
methods
in response
to thetocontrol
target.
In
1). The key to analyzing this issue lies in the regulatory methods in response to the control target. In
is
worth
noting
that
when
the
combination
of of
GMSES-MFR
andand
GMSES-CES
resources
is
In addition,
addition,itititis
isworth
worthnoting
notingthat
thatwhen
when
the
combination
GMSES-MFR
GMSES-CES
resources
the combination of GMSES-MFR and GMSES-CES resources is
adopted,
a
better
control
effect
can
be
achieved,
as
the
day-ahead
tie-line
power
(the
black
line)
is
is adopted,
control effect
effectcan
canbe
beachieved,
achieved,asasthe
theday-ahead
day-aheadtie-line
tie-line
power
(the
black
line)
adopted, aa better
better control
power
(the
black
line)
is is
well
tracked
by
Case
2 (theblue
blueline)
line)ininFigure
Figure15.
15. This
This is
because
the
energy
conversion
and
dispatch
well
tracked
by
Case
2
(the
is
because
the
energy
conversion
and
dispatch
well tracked by Case 2 (the blue line) in Figure 15. This is because the energy conversion and dispatch
of GMSES-MFR
are limiteddue
due tothe
the BEHequipment
equipment capacity,
while
the
flexible
use
of GMSES-CES
of of
GMSES-MFR
are
capacity,while
whilethe
theflexible
flexibleuse
use
GMSES-CES
GMSES-MFR
arelimited
limited dueto
to theBEH
BEH equipment capacity,
ofof
GMSES-CES
can
compensate
for
the
margin
in
power
regulation.
The
collaborative
utilization
of
MFR
and
CES
cancan
compensate
The collaborative
collaborativeutilization
utilization
MFR
and
CES
compensatefor
forthe
themargin
marginininpower
powerregulation.
regulation. The
ofof
MFR
and
CES
could
achieve
a
more
stable
control
effect
of
the
tie-line
power
fluctuations.
could
achieve
a more
fluctuations.
could
achieve
a morestable
stablecontrol
controleffect
effectof
ofthe
the tie-line
tie-line power fluctuations.

Figure 15. Tie-line power fluctuations smoothing results in the intra-hour scenario.
Figure15.
15.Tie-line
Tie-linepower
powerfluctuations
fluctuations smoothing
smoothing results
Figure
resultsin
inthe
theintra-hour
intra-hourscenario.
scenario.

The functions of GMSES-CES are realized by regulating the SOC. As shown in Figure 16, SOC
The
functionsofofGMSES-CES
GMSES-CESare
are realized
realized by regulating
Figure
16,16,
SOC
The
functions
regulatingthe
theSOC.
SOC.As
Asshown
showninin
Figure
SOC
can respond to the scheduling target within the operation boundaries between 15% and 85%. Taking
can
respond
to
the
scheduling
target
within
the
operation
boundaries
between
15%
and
85%.
Taking
can respond to the scheduling target within the operation boundaries between 15% and 85%. Taking
the period ta as an example, when the GMSES-MFR is adopted alone, the regulated tie-line power
period
a as
anexample,
example,when
whenthe
the GMSES-MFR
GMSES-MFR is
alone,
the
tie-line
power
thethe
period
ta tas
is adopted
adopted
alone,
theregulated
regulated
tie-line
power
(the
red line
inan
Figure
15) is higher than
that of day-ahead
scheduling
baseline
(the black line
in Figure
(the red line in Figure 15) is higher than that of day-ahead scheduling baseline (the black line in Figure
15). To improve this situation, the main trend of GMSES-CES enters into the discharging status by
15). To improve this situation, the main trend of GMSES-CES enters into the discharging status by
decreasing its SOC, which facilitates the regulated tie-line power to fit the baseline. The opposite
decreasing its SOC, which facilitates the regulated tie-line power to fit the baseline. The opposite
operation can be found in the period tb. Relevant results of GMSES-MFR regulation are shown in
operation can be found in the period tb. Relevant results of GMSES-MFR regulation are shown in
Figures 16 and 17. The integrated GMSES VSOC results are shown in Figure 18, indicating that the
Figures 16 and 17. The integrated GMSES VSOC results are shown in Figure 18, indicating that the
GMSES operation constraints are satisfied.
GMSES operation constraints are satisfied.

Energies 2019, 12, 246

19 of 28

(the red line in Figure 15) is higher than that of day-ahead scheduling baseline (the black line in
Figure 15). To improve this situation, the main trend of GMSES-CES enters into the discharging status
Energies
2019, 11, xits SOC, which facilitates the regulated tie-line power to fit the baseline. The opposite
19 of 29
by decreasing
operation can be found in the period tb . Relevant results of GMSES-MFR regulation are shown in
Figures 16 and 17. The integrated GMSES VSOC results are shown in Figure 18, indicating that the
GMSES operation
constraints are satisfied.
Energies 2019, 11, x
19 of 29
Energies 2019, 11, x

19 of 29

16. Results
of GMSES-CES SOC
GMSES-MFR
VSOC.VSOC.
Figure
16.
of
and
GMSES-MFR
FigureFigure
16. Results
Results
of GMSES-CES
GMSES-CES SOC
SOCand
and
GMSES-MFR
VSOC.

Figure 16. Results of GMSES-CES SOC and GMSES-MFR VSOC.

Figure 17. BEH regulation results in Case 2.

Figure
BEHregulation
regulation results
inin
Case
2. 2.
Figure
BEH
regulation
results
Figure
17.17.BEH
results
Case

Figure 18. Results of integrated GMSES VSOC.

The power deviation is further eliminated when the GMSES-MFR and GMSES-CES resources
are utilized together. However, this also brings challenges of calculating burden due to the increase
Figure
Results of
of integrated
integrated GMSES
VSOC.
Figure
18.18.Results
GMSES
VSOC.

The power deviation is further eliminated when the GMSES-MFR and GMSES-CES resources
Figure 18. Results of integrated GMSES VSOC.
are utilized together. However, this also brings challenges of calculating burden due to the increase

The power deviation is further eliminated when the GMSES-MFR and GMSES-CES resources
are utilized together. However, this also brings challenges of calculating burden due to the increase

Energies 2019, 12, 246

20 of 28

The power deviation is further eliminated when the GMSES-MFR and GMSES-CES resources
are utilized together. However, this also brings challenges of calculating burden due to the increase
Energies
20 of
Energies2019,
2019,11,
11,xx
of 29
29
of decision
variables
and constraints handling. Therefore, the IEDS dispatcher should20select
the
appropriate
scheduling
strategies
according
to
the
target
accuracy
and
calculation
time.
of decision variables and constraints handling. Therefore, the IEDS dispatcher should select the
of decision variables and constraints handling. Therefore, the IEDS dispatcher should select the
appropriate
appropriate scheduling
scheduling strategies
strategies according
according to
to the
the target
target accuracy
accuracy and
and calculation
calculation time.
time.

4.3. Case 3: Ultra-Short-Term Energy Balance

4.3.
Case
3:
Energy
Balance
4.3.the
Case
3:Ultra-Short-Term
Ultra-Short-Term
Energy
Balancescenario, the GMSES-DRR is used to smooth the irregular
In
ultra-short-term
energy
balance
In
energy
scenario,
the
is
to
the
power fluctuations
under a reasonable
controlling
method.
It is assumed
the users
have signed
Inthe
theultra-short-term
ultra-short-term
energybalance
balance
scenario,
theGMSES-DRR
GMSES-DRR
isused
usedthat
tosmooth
smooth
theirregular
irregular
power
fluctuations
under
a
reasonable
controlling
method.
It
is
assumed
that
the
users
have
signed
contracts
to fluctuations
participate in
the ademand
response
program
with
IEDS. Inthat
thisthe
regard,
the DRR
can be
power
under
reasonable
controlling
method.
It the
is assumed
users have
signed
contracts
to
participate
in
the
demand
response
program
with
the
IEDS.
In
this
regard,
the
DRR
can
contracts toto
participate
response
program
with 19,
the aIEDS.
In this regard,
can
fully regulated
respond in
to the
thedemand
scheduling
signal.
In Figure
comparison
aboutthe
theDRR
scheduling
be
fully
regulated
to
the
In
Figure
19,
about
the
bein
fully
regulated
to respond
respond
to
the scheduling
scheduling
signal.
Inpower
Figure fluctuations
19, aa comparison
comparison
about
the
results
cases
mentioned
above to
suggests
that thesignal.
tie-line
are well
smoothed
scheduling
results
in
cases
mentioned
above
suggests
that
the
tie-line
power
fluctuations
are
well
scheduling
results
in
cases
mentioned
above
suggests
that
the
tie-line
power
fluctuations
are
well
by the GMSES-DRR. A few fluctuations show up because the DRR target has reached its regulation
smoothed
smoothed by
by the
the GMSES-DRR.
GMSES-DRR. A
A few
few fluctuations
fluctuations show
show up
up because
because the
the DRR
DRR target
target has
has reached
reached its
its
boundaries. The application of the general parameter serialization (GPS)-based control strategy has
regulation
boundaries.
The
application
of
the
general
parameter
serialization
(GPS)-based
regulation boundaries. The application of the general parameter serialization (GPS)-based control
control
caused
GMSES-DRR
to provide the to
required power
increase or decrease.
In addition,
based on the
strategy
strategy has
has caused
caused GMSES-DRR
GMSES-DRR to provide
provide the
the required
required power
power increase
increase or
or decrease.
decrease. In
In addition,
addition,
response
characteristics
of each
controllable
GMSES-DRR
can GMSES-DRR
be
well controlled
respond
based
on
characteristics
of
each
element,
can
based
on the
the response
response
characteristics
of element,
each controllable
controllable
element,
GMSES-DRR
can be
betowell
well
to minute-level
power
fluctuations
within
operation
boundaries
in
Figure
20.
controlled
to
respond
to
minute-level
power
fluctuations
within
operation
boundaries
in
Figure
20.
controlled to respond to minute-level power fluctuations within operation boundaries in Figure 20.

Figure
Tie-line
power
fluctuations
results.
Figure
19.
Tie-linepower
powerfluctuations
fluctuationssmoothing
smoothing
results.
Figure
19.19.
Tie-line
smoothing
results.

(a)
(a)DRR
DRRload
loadresponse
responsein
innode
node712
712

(b)
(b)DRR
DRRload
loadresponse
responsein
innode
node713
713

(c)
(c)DRR
DRRload
loadresponse
responsein
innode
node720
720

(d)
(d)DRR
DRRload
loadresponse
responsein
innode
node735
735

Figure
20.
resultsof
ofDRRs.
DRRs.
Figure
20.
The
response
Figure
20.The
Theresponse
responseresults
results
of
DRRs.

Energies 2019, 12, 246

21 of 28

Energies 2019, 11, x

21 of 29

21 ofIt 29
constraints,
the
VSOC
GMSES-DRR
With these operation
constraints,
the
VSOCresults
resultsofof
GMSES-DRRare
areshown
shownininFigure
Figure21.
21. Itcan
becan
seen
that
the
CAC
power
is
highest
between
8:00
and
18:00
in
Figure
20,
which
comprises
most
be seen that the CAC power is highest between 8:00 and 18:00 in Figure 20, which comprises most
With these operation constraints, the VSOC results of GMSES-DRR are shown in Figure 21. It
of of
thethe
total
output
the VSOC
VSOCof
ofDRR
DRRbecomes
becomesthe
the
highest
Figure
total
outputpower
powerofofDRR.
DRR. As
As aa result,
result, the
highest
in in
Figure
21,21,
can be seen that the CAC power is highest between 8:00 and 18:00 in Figure 20, which comprises most
indicating
that
the
indicating
that
thevirtual
virtualstored
storedenergy
energyisisalmost
almost full.
of the total output power of DRR. As a result, the VSOC of DRR becomes the highest in Figure 21,
indicating that the virtual stored energy is almost full.

GMSES-DRR VSOC

Energies
11,operation
x
With2019,
these

Figure21.
21. Results
Results of
Figure
of GMSES-DRR
GMSES-DRRVSOC.
VSOC.

Figure 21. Results of GMSES-DRR VSOC.

addition,totomeasure
measurethe
thesmoothing
smoothing effect of
tie-line
power
InIn
addition,
of tie-line
tie-line power
powerfluctuations,
fluctuations,the
the
tie-line
power
control
error
err
z
and
the
tie-line
power
deviation
R
z
are
defined
in
Equations
(42)
and
(43).
control In
error
errz and
tie-line
power
deviation
defined
in Equations
(42)the
and
(43). power
addition,
to the
measure
the
smoothing
effectRzofare
tie-line
power
fluctuations,
tie-line

Pex,t − Pex,t Rz are defined'' in Equations (42) and (43).
control error errz and the tie-line power
errz ,t deviation
=
set − set
(42)
Pex,t
Pz z  100% t  T
setPex,t ex,t
00
Pex,

P
errz,t =
×
100%

t

T
(42)
t
ex,
t
''
errz ,t = Pset set
× 100% ∀t ∈ T
(42)
ex,t
Pzex,t
set
''
Rz ,t = Pex,t − Pex,t t  T
(43)
z

set
00


z
set
''
− Pex,t ∀scheduling
z,t =
Rz,t P=ex,t
Pintra-hour
t∀∈t T∈ T
(43)(43)
where z is the application scenario,Rincluding
(using GMSES-MFR only), Case
ex,t − Pex,t
z
2 (using
GMSES-MFR
and
GMSES-CES),
and Case 3 (using
GMSES-DRR); GMSES-MFR
power
Pex , t is the tie-line
where
z iszthe
application
scenario,
including
only),
Case 2
where
is the
application
scenario,
includingintra-hour
intra-hourscheduling
scheduling(using
(using GMSES-MFR
only),
Case
under
the
application
scenario
z
at
the
tth
minute.
The
tie-line
power
control
error
err
z under the
z
z
(using
GMSES-MFR
and
GMSES-CES),
and
Case
3
(using
GMSES-DRR);
P
is
the
tie-line
power
2 (using GMSES-MFR and GMSES-CES), and Case 3 (using GMSES-DRR); Pexex,t
is the tie-line power
,t
above scenario is shown in Figure 22.
under
thethe
application
scenario
z atzthe
tth minute.
TheThe
tie-line
power
control
errorerror
errz under
the above
under
application
scenario
at the
tth minute.
tie-line
power
control
errz under
the
above is
scenario
Figure 22.
scenario
shownisinshown
Figurein22.
set

z

Figure 22. Tie-line power control error results.

Figure
error results.
results.
Figure22.
22.Tie-line
Tie-linepower
power control error

In Figure 22, it can be concluded that the tie-line power control error decreases from the use of
GMSES-MFR, GMSES-MFR and GMSES-CES, to GMSES-DRR. The correction of power fluctuations
In In
Figure
22,22,
it can
bebeconcluded
controlerror
errordecreases
decreasesfrom
fromthe
the
use
Figure
it can
concludedthat
thatthe
thetie-line
tie-line power
power control
use
of of
for the updated data in the ultra-short-term is the best. In addition to the tie-line power, the operation
GMSES-MFR,
GMSES-MFRand
andGMSES-CES,
GMSES-CES, to
to GMSES-DRR.
GMSES-DRR. The
GMSES-MFR,
GMSES-MFR
Thecorrection
correctionofofpower
powerfluctuations
fluctuations
and regulation costs of IEDS are shown in Figure 23a, which is essential to guaranteeing accurate
updated
data
theultra-short-term
ultra-short-termisisthe
the best.
best. In addition
operation
forfor
thethe
updated
data
inin
the
additionto
tothe
thetie-line
tie-linepower,
power,the
the
operation
and
regulation
costs
of
IEDS
are
shown
in
Figure
23a,
which
is
essential
to
guaranteeing
accurate
and regulation costs of IEDS are shown in Figure 23a, which is essential to guaranteeing accurate

Energies 2019, 12, 246

22 of 28

IEDS operation. The GMSES regulation costs Cz , composed of Cz1 , Cz2 , Cz3 , are calculated by
Equations
(44)–(46).
Energies 2019,
11, x
22 of 29
0

z2, C z3, are calculated by Equations
IEDS operation.T TheπGMSES
regulation

z, composed
z1, C
πe−sell,t costs
e−buy,t +
e−buy,t − πof
e−C
sell,t
z1
z1
z1
) ∀ t ∈ T0
(44)
Cz1 = ∑ (
Pe,t
+
Pe,t + πg,t Pg,t
(44)–(46).
2
2
t =1

T'

Cz1 = (

Cz2

π e-buy,t + π e-sell,t

Pe,z1t +

π e-buy,t - π e-sell,t

Pz1 + π Pz1 ) ∀t ∈ T'

g,t g,t


e,t
2
πe−buy,t + πe−sell,tt =1 z2
e−buy,t − πe−sell,t z2
z2
z2
= ∑(
+ πCES PCES,t
Pe,t +
)
Pe,t + πg,t Pg,t
2 T π e-buy,t + π e-sell,t z2 π e-buy,
2 t - π e-sell,t z2
t =1
z2
z2
'

T0

2
π

'

Cz2 = (

2

t =1

Cz3 =

Pe,t +

T00

M

∑(∑
T''



2

(44)

∀t ∈ T0 (45)

Pe,t + π g,t Pg,t + π CES PCES,t ) ∀t ∈ T




t
Cm βtm Pmt−1 − Pm,tar
)
M

(45)

∀t ∈ T00

(46)

t
) ∀t ∈ T''
Cm ⋅ βmt ⋅ Pmt −1 − Pm,tar

tC
=z31 =m=(1

(46)

t =1 m =1

where Cz1 , Cz2 , Cz3 represent the GMSES regulation cost of using MFR, using MFR and CES (Case 2),
where Cz1 , C z2 , C z3 represent the GMSES regulation cost of using MFR, using MFR and CESz2(Case
and 2),
using
DRR (Case 3) in a day respectively; πCES is the
regulation cost of using CES; PCES,t
is the
z2
and using DRR (Case 3) in a day respectively;
is
π CES is the regulation cost of using CES; P
CES,t
charging
and
discharging
power
in
Case
2
at
period
t.
the charging and discharging power in Case 2 at period t.
Operation cost Ccost

GMSES resources regulation cost Cz
8174.88

8000
7614.88

Cost ($)

7500
7000

6500

7306.41
GMSES regulation
cost increased

6285.37

6000

Maximum tie-line power deviation Rz(kW)

8500

120
100

90.73

80
60
Power fluctuation
deviation decreased

40

24.27
20
3.00

0

MFR
(Day-ahead)

MFR
(Intra-hour)

MFR + CES
DRR
(Intra-hour) (Ultra-short-term)

(a) Operation cost of IEDS and regulation costs of GMSES

0

MFR Rz1
(Intra-hour)

MFR + CES Rz2
(Intra-hour)

DRR Rz3
(Ultra-short-term)

(b) Maximum tie-line power deviation of IEDS

Figure
23. 23.
(a) (Operation
cost
costsofofGMSES;
GMSES;(b(b)
maximum
tie-line
power
Figure
a) Operation
costofofIEDS
IEDSand
and regulation
regulation costs
) maximum
tie-line
power
deviation
of IEDS.
deviation
of IEDS.

TheThe
GMSES
regulation
costs
increase
as the
response
resource
varies
from
MFR
to DRR,
as shown
GMSES
regulation
costs
increase
as the
response
resource
varies
from
MFR
to DRR,
as
shown
in
Figure
23a.
However,
it
is
worth
pointing
out
that
the
tie-line
power
deviation
is
decreased,
in Figure 23a. However, it is worth pointing out that the tie-line power deviation is decreased, reducing
reducing the
influence
of the underlying
IEDS
on the
upper
energy The
system.
The fluctuations
of
the influence
of the
underlying
IEDS on the
upper
energy
system.
fluctuations
of renewable
renewable
energy
and
loads
are
accommodated
by
GMSES
resources
and
the
relevant
energy
energy and loads are accommodated by GMSES resources and the relevant energy scheduling strategy,
scheduling strategy, which contributes to stable operation, as well as improving the power quality.
which
contributes to stable operation, as well as improving the power quality. In the ultra-short-term
In the ultra-short-term energy balance, although the regulation cost of using DRR is high, the tie-line
energy balance, although the regulation cost of using DRR is high, the tie-line power deviation reaches
power deviation reaches its lowest value (depicted in Figure 23b). This is because the regulation cost
its lowest
value
(depicted
in Figure
23b).
is because
the regulation
cost hand,
of per-unit
of per-unit
DRR
is relatively
high, and
DRRThis
is regulated
frequently.
On the other
due to DRR
the is
relatively
high,
and DRR isand
regulated
frequently.
other hand,
to the real-time
coordination
real-time
coordination
regulation
of DRR, On
the the
aperiodic
powerdue
fluctuations
of the renewable
and energy
regulation
of DRR,
thebeaperiodic
and loads
could
smoothedpower
out. fluctuations of the renewable energy and loads could be
In out.
summary, the three kinds of resources in GMSES should be applied in different scenarios
smoothed
owing
to
their response
and application
GMSES-MFR
could
In summary,
the threecharacteristics
kinds of resources
in GMSESdemands.
should beSpecifically,
applied inthe
different
scenarios
owing
be
used
in
day-ahead
optimal
scheduling,
such
as
economic
scheduling
and
loss
reduction.
Theused
to their response characteristics and application demands. Specifically, the GMSES-MFR could be
GMSES-MFR and GMSES-CES are suitable for intra-hour scheduling with relatively high adjustment
in day-ahead optimal scheduling, such as economic scheduling and loss reduction. The GMSES-MFR
accuracy. As for the ultra-short-term energy balance, the GMSES-DRR can be selected to meet highand GMSES-CES are suitable for intra-hour scheduling with relatively high adjustment accuracy.
precision requirement with sufficient regulation costs and calculating time.
As for the ultra-short-term energy balance, the GMSES-DRR can be selected to meet high-precision
requirement
with sufficient regulation costs and calculating time.
5. Conclusions
This paper proposes a generalized multi-source energy storage (GMSES) model that includes
5. Conclusions

resources of conventional energy storage, multi-energy flow and demand response. Aggregation and
This paper of
proposes
a generalized
multi-source
storage
(GMSES)
modelinvestment
that includes
coordination
GMSES resources
show the
potential ofenergy
equivalent
energy
storage, where
resources
of conventional
energycan
storage,
multi-energy
flow in
and
response.
Aggregation
in conventional
energy storage
be reduced.
Uncertainties
thedemand
renewable
energy and
energy

Energies 2019, 12, 246

23 of 28

and coordination of GMSES resources show the potential of equivalent energy storage, where
investment in conventional energy storage can be reduced. Uncertainties in the renewable energy
and energy loads bring challenges to IEDS operation on multiple time scales. To solve this problem,
the GMSES hierarchical optimal scheduling framework is studied, including day-ahead, intra-hour,
and ultra-short-term scheduling. To be specific, operation costs are minimized in day-ahead scheduling
due to the complementary nature of multi-carrier energy prices, and the set-points can be generated,
as well as the basic tie-line power. Various GMSES resources are called upon in both intra-hour
scheduling and the ultra-short-term scenario, where power deviation caused by forecast error can be
regulated, and energy balance service can be provided.
It is worth mentioning that the general parameter serialization (GPS)-based control strategy
was studied to determine the responsive group and priority sequence in ultra-short-term scheduling.
Demand response resources (including heat pumps, central air conditioning and electric vehicles) can
be integrated in a flexible way for power fluctuation smoothing.
The proposed hierarchical scheduling strategy is conducted in a modified electricity-gas coupled
IEDS. Numerical results have shown the effectiveness of the co-optimization GMSES model in reducing
the impacts on the upper layer energy system; in addition, the operation costs and the tie-line power
fluctuations can be minimized by GMSES. The comparison of GMSES resources is given through the
scheduling results, showing the applicability and scalability in multi-type multi-timescale regulation,
which contributes to the decision making of IEDS dispatch center.
Author Contributions: W.W., D.W. and L.L. conceived and designed the study; W.W., D.W. and L.L. performed
the study; H.J., Y.Z., Z.M., W.D. reviewed and edited the manuscript; W.W. and L.L. wrote the paper. All authors
read and approved the manuscript.
Funding: This research received no external funding.
Acknowledgments: This work was supported by National Key R&D Program of China (No. 2018YFB0905000),
Joint Research Fund of the National Science Fund of China (U1766210), “131” Talent & Innovative Team of Tianjin
City and National Social Science Foundation of China (12&ZD208). This study was conducted in cooperation of
APPLIED ENERGY UNiLAB-DEM: Distributed Energy & Microgrid. UNiLAB is an international virtual lab of
collective intelligence in Applied Energy.
Conflicts of Interest: The authors declare no conflict of interest.

Nomenclature
Notation
SSOC_k (t)
∆SSOC_k (t)
SSOC_k , SSOC_k
∆SSOC_k , ∆SSOC_k
CES , PCES
Pk,C
k,D
ηk,C , ηk,D
CES
Wk,rated
SSOC_k (0), SSOC_k (T)
Pe , Pg
Le , Lh
e
h
ηCHP
, ηCHP

η AC , η GB
λC , λD
lb, ub
e,max
e,max
PCHP
, PAC
ANGS
Qr , ωs , ωl

Description
State of charge (SOC) of the kth energy storage unit at period t
SOC variation of the kth energy storage unit at period t
Upper and lower boundaries of SOC of the kth energy storage unit
Upper and lower boundaries of SOC variation of the kth energy storage unit
Charging and discharging power of the kth conventional energy storage (CES) unit
Charging and discharging efficiency of the kth CES unit
Rated power of the kth CES unit
The beginning and the end of SOC of the kth CES unit
Electricity and natural gas power input of bi-directional energy hub (BEH)
Electrical and thermal loads of BEH
Gas-electricity energy conversion efficiency and gas-heat energy conversion
efficiency in combined heat and power (CHP)
Energy conversion efficiency of air-conditioner system and gas boiler (GB)
Dispatch factors of BEH in charging/standby, discharging status
Subscript of lower and upper boundaries
Maximum output power of CHP, and air-conditioner system
The branch-nodal incidence matrix of natural gas system (NGS)
A vector of mass flow rates through branches, a vector of gas supplies and gas
demands at each node of NGS

Energies 2019, 12, 246

with , ω with
ωl,i
l,j
without , ω without , GHV
ωl,i
l,j

∆pr
Dp , f r , Lr , Sg , v g
τon , τoff , τidle , τcharge
mn
h
Emn , Em
n
H
t+∆t
REm
n ,h
M, N
TPo , Qta , Fbt
O, A, B
h , Ph
Pm
n
mn ,rated
h
ηm
n
Qta,+ , Qta,−
U, V
Ccost

CEPS,t , CNGS,t , CBEH,t
πe−buy,t , πe−sell,t , π g,t
PEPS,t , PNGS,t
Pe,t
Pg,t
T, T0 , T00
set , P
Pex,t
ex,t
xEPS , xNGS , xBEH
xEPS_max , xEPS_min
xNGS_max , xNGS_min
xBEH_max , xBEH_min
M N

M N

m n =1

m n =1

24 of 28

Gas demand at node i and node j with connected BEH
Gas demand at node i and node j without connected BEH, and gross heating
value (GHV)
Pressure drop along the pipe of NGS
Diameter of pipe, friction factor, length of pipe, gas specific gravity, and gas flow
rate of NGS
Equipment operation status (open/off/idle) in DRR, charging status of
electric vehicles
The nth responsive load for type m in demand response resource (DRR)
Set of operation status in DRR, the hth operation status of mn in DRR
Numbers of operation status of mn in DRR
h at period t + ∆t
DRR physical model at operation status Em
n
Set of responsive load types including heat pump, electric vehicle and central air
conditioning, total response numbers of each DRR types
The oth parameter of DRR physical characteristics, the ath key operation parameter
and the bth control variable of mn in DRR
Numbers of physical characteristics, key operation parameters and control
variables of mn in DRR
The hth operation power and rated power of mn in DRR
The hth load efficiency factor of mn in DRR
Upper and lower boundaries of the ath key operation parameter Qta
Operation status of DRR when Qta ≤ Qta,− or Qta ≥ Qta,+
Daily operation costs of integrated energy distribution system
Operation costs of conventional loads in electric power system (EPS) and NGS, and
coupled loads in BEH at period t
Electricity prices to purchase and sell, gas price to purchase
Electric and gas power consumed by conventional electric loads and conventional
gas loads at period t
Interactive electric power in BEH at period t
Consumed gas power in BEH at period t
Scheduling periods of hours, 15 minutes, 1 minute
Target setting tie-line power, actually optimized tie-line power
The set of variables of EPS, NGS and BEH
Upper and lower boundaries of EPS variables
Upper and lower boundaries of NGS variables
Upper and lower limits of the equipment output considering component capacities
of BEHs

mn ,t
mn ,t
, ∑ ∑ ∆Pdown,σ
Upward and down regulations of the total DRR groups in node σ at period t
∑ ∑ Pup,σ
t
PD,σ
Cm
t −1
Pm
t
Pm,tar
t
t
Pm,up
, Pm,down
IEDS
GMSES
CES
MFR
DRR
GPS
EPS
NGS
DHS

Demand side power regulation in node σ at period t
Controlled price for the type m load in DRR
Power consumption of the type m load in DRR at period t − 1
Response target for the type m load in DRR at period t
Upward and down regulations for the type m load of DRR groups at period t
Integrated energy distribution system
Generalized multi-source energy storage
Conventional energy storage
Multi-energy flow resource
Demand response resource
General parameter serialization (GPS)-based control strategy
Electric power system
Natural gas system
District heating system

Acknowledgments: This work was supported by National Key R&D Program of China (No. 2018YFB0905000),
Joint Research Fund of the National Science Fund of China (U1766210), “131” Talent & Innovative Team of
Tianjin City and National Social Science Foundation of China (12&ZD208). This study was conducted in
cooperation of APPLIED ENERGY UNiLAB-DEM: Distributed Energy & Microgrid. UNiLAB is an international
virtual lab of collective intelligence in Applied Energy.

Energies 2019, 12, 246

25 of 28

Conflicts of Interest: The authors declare no conflict of interest.

Appendix A
Appendix A

Energies 2019, 11, x
Energies 2019, 11, x

Figure A1.
A1. Illustration of IEDS-GMSES
IEDS-GMSES co-simulation
co-simulation platform.
platform.
Figure

Figure A2. Load
forecast data of BEH.
Figure A2. Load forecast data of BEH.

Figure A3.
A3. Loadforecast
forecast data of
of BEH.
Figure
Figure A3. Load
Load forecast data
data of BEH.
BEH.

24 of 29
24 of 29

Energies 2019, 12, 246

26 of 28

Figure A3. Load forecast data of BEH.

Figure
FigureA4.
A4. Energy
Energyprice.
price.

Type
Type
HP
HP

Table A1. The number of DRR at per node.
Table A1. The number of DRR at per node.
Node
Node
712
713
720

712

Phase A

713

720

Phase C

120
0
0

EV

Phase A
Phase B
Phase C

0
350
0

0
0
400

300
0
0

0
0
320

CAC

Phase A
Phase B
Phase C

25
0
0

0
0
20

0
0
22

0
14
0

Phase
A B
Phase

120

0

735

735

150

0
0
100
0

100
0 100
0

0 0

Table A2. Simulation parameters of DRR [34,35,40].
Type

HP

EV

CAC

Parameter Value

Parameter Name

Parameter Value

Average equivalent thermal
resistance/(◦ C/W)

Parameter Name

0.121

Average equivalent thermal
capacitance/(J/◦ C)

3599.3

Average equivalent heat
ratio/W

400

Rated power/kW

6

Initial
temperature/◦ C

21

Temperature deadband/◦ C

4

Regulation cost/($/kWh)

0.230

Controlled period/min

1

Energy state upper
boundary

0.0125

Energy state lower
boundary

−0.0125

Charging power/kW

5

Charging efficiency

95%

Regulation cost/($/kWh)

0.155

Battery capacity/kWh

5.00~20.00

Energy state deadband

0.025

Controlled period/min

1

Average energy efficiency
ratio

5

Average rated power/kW

40

Coefficient of low
consumption

0.1

Initial room temperature/◦ C

24

Temperature deadband/◦ C

5

Range of gear numbers

[3,10]

Regulation cost/($/kWh)

2.797

Standard deviation of gear
numbers

2.07

Controlled period/min

5

Energies 2019, 12, 246

27 of 28

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