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Multi-agent-based sharing power economy for a smart
community
Danish Mahmood
1
, Nadeem Javaid
1,
*
,†
, Imran Ahmed
2
, Nabil Alrajeh
3
,
Iftikhar Azim Niaz
1
and Zahoor Ali Khan
4
1
COMSATS Institute of Information and Technology, Islamabad, 44000, Pakistan
2
Institute of Management Sciences, Peshawar, 25000, Pakistan
3
College of Applied Medical Sciences, Department of Biomedical Technology, King Saud University, Riyadh, 11633, Saudi Arabia
4
Computer Information Science, Higher Colleges of Technology, Fujairah Campus, 4114, UAE
SUMMARY
In this paper, a multi-agent-based locally administrated power distribution hub (PDH) for social welfare is proposed that
optimizes energy consumption, allocation, and management of battery energy storage systems (ESSs) for a smart
community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage
capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH.
PDH is composed of multiple small-scale battery ESSs (each owned by community users), which are connected together to
form a unified-ESS. Proposed PDH offers a localized switching mechanism that takes decision of whether to buy electricity
from utility or use unified-ESS. This decision is based on the price of electricity at ‘time of use’and ‘state of charge’of
unified-ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing
or decrementing respective submeters. There is no buying or selling of power from PDH; there is power sharing with
the concept of ‘no profit, no loss’. The objective of the proposed PDH is to limit the purchase of electricity on ‘high priced’
hours from the utility. This not only benefits the utility at crucial hours but also provides effective use of power at the
demand side. The proposed multi-agent system depicts the concept of sharing power economy within a community.
Finally, the proposed model is analyzed analytically, considering on-peak, off-peak, and mid-level (mid-peak) prices of
a real-time price signal during 24 h of a day. Results clearly show vital financial benefits of ‘sharing power economy’
for end users and efficient use of power within the smart community. Copyright © 2017 John Wiley & Sons, Ltd.
KEY WORDS
DSM; battery; energy storage systems; sharing economy; multi-agent systems; smart community; smart homes
Correspondence
*Nadeem Javaid, COMSATS Institute of Information Technology, Park Road, Islamabad 44000, Pakistan.
†
E-mail: nadeemjavaidqau@gmail.com; http://www.njavaid.com
Received 15 January 2017; Revised 9 April 2017; Accepted 10 April 2017
1. INTRODUCTION
Minimizing the use of hydrocarbons for power generation
is the need of era owing to ecological and geopolitical
concerns. To meet the ever-increasing demand of electric
power, renewable energy sources are gaining attention
rapidly. However, power generation by renewable sources
is intermittent in nature, which introduced numerous
challenges such as power network stability, reliability,
and quality. Energy storage systems (ESSs) can be one of
the most valid answers to these challenges. Besides the
importance of ESSs owing to renewable sources, since
the revelation of electric power, persuasive techniques to
store electricity are developing. In the course of the past
few decades, the power storage industry has kept on
developing to meet dynamic and ever-changing energy
requirements and technological advancements. Moreover,
battery ESSs (BESSs) give a wide horizon in the
management of energy consumption to support more
resilient power infrastructure and financial benefits to
residential users as well as utilities. Within a power
network, BESSs have vast potential to offer a number of
services. Such a system not only provides flexibility in
existing power infrastructure but also increases system
reliability. It is also one of the most promising solutions
to minimize power consumption (PC) peaks as well [1–5].
Considering BESSs in residential units, power may be
stored from small-scale renewable sources or by smart grid
during low-priced hours. Either way, BESSs give
efficiency in PC management and tends to reduce
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
Int. J. Energy Res. (2017)
Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/er.3768
Copyright © 2017 John Wiley & Sons, Ltd.
electricity bills by using widely appraised demand-side
management (DSM) strategies [6–8]. The residential sector
is a noteworthy component of overall PC, and if every
home is equipped with a small-scale BESS, this tends to
minimize global problems like rapid consumption of fossil
fuels and carbonizing factor.
On the other hand, the concept of sharing economy is
emerging that tends to attract masses in every field of life.
Alex Stephany, a sharing economy expert, stated that
there is no evidence when this terminology was first used;
hence there are multiple definitions [9]. Nonetheless,
broadest of all can be ‘the people who share’that includes
economies based on ‘demand’[10]. In sharing economy,
people actively participate as providers and takers
simultaneously, which is often web based [11,12]. For
more information regarding smart communities/cities and
sharing economy, please refer to a European university
working paper [13].
Tables I and II give abbreviations and mathematical
notations used in this paper, respectively.
2. RELATED WORK
Battery energy storage systems are well suited for the
future distributed power systems. Extensive research is in
progress that explores the benefits of BESSs considering
residential PC [14,15]. BESSs are capable of not only
lowering electricity bills for end users [16] but also taking
part in tackling the intermittency problem of renewable
energy generation [17]. Limiting only to residential sector,
researchers have studied widely the impact of BESS
installed at individual residential units, and residents are
the sole users of this BESS. Numerous studies are also
conducted for optimal charging pattern of electric vehicles
(EVs), which are also considered as a mode for energy
transportation [18]. There is a common aspect in the
aforementioned studies; that is, only one entity owns BESS
at a time. The concept of shared BESS is given in [5] that
offers benefits of joint BESS.
Table III presents state-of-the-art literature regarding
energy management of residential sector.
Centralized controlled mechanisms for energy
management have been studied in literature. It is easier
and more feasible to develop a framework that is able to
carry economic dispatch for distributed power generation
sources along with BESSs [19] with respect to time. As
system size and the number of components expand with
the passage of time, such framework has a tendency to
become more complex and error prone, mainly owing to
prediction errors. Moreover, reliability is a major concern,
and any error may choke the whole centralized system
[19]. For this issue to be addressed, expert systems and
heuristic techniques are utilized; however, in this case,
flexibility and scalability of the system are still
compromised [20,21]. A major solution to all these
concerns lie within distributed control systems that give
liberty of local decision power, and multi-agent systems
(MASs) promise real-time and localized control with
flexibility, scalability, and fault-tolerant behavior [22].
2.1. Multi-agent system in the power sector
Multi-agent system is a state-of-the-art technology that
offers solutions to numerous domains of life that includes
diagnostics, monitoring, controlling and sensing,
networks, and automation. Considering artificial
intelligence, MAS seems to be a viable solution that
decentralizes power to each agent. This enables MAS
technology to tackle many issues including lowering
complexity and increasing scalability.
Considering distributed topology of future power
network, solutions have been proposed that integrate new
components to ensure efficient communication among
existing and forthcoming technologies. MAS is advocated
as a useful tool considering a wide range of applications.
Among these applications, in the past few years, MAS
has gained remarkable popularity in the energy
management domain. Autonomous control of networked
power grid environment allows additional distributed
generation resources without re-fabricating the whole
system. Moreover, peer-to-peer communication model
within different distributed generation sources eliminates
the requirement of centralized controller. Under such
environment, Hu et al. [29] reported that MAS is one of
the most inspiring and rapidly growing tool.
Hu et al. [30] utilized MAS for distributed power
congestion management with the integration of EVs as
Table I. Abbreviations.
State of charge SoC Single smart home k
Smart homes agreed on sharing power economy nPower distribution hub PDH
Energy storage system ESS Battery energy storage system BESS
Multi-agent system MAS Demand-side management DSM
Distributed generation DG Power consumption PC
Electric vehicle EV Photovoltaic PV
Distributed energy resource DER Carbon dioxide CO
2
Power sharing PS Mixed integer linear programming MILP
Energy management system EMS Alternate energy resource AER
Home energy management system HEMS Power distribution system PDS
Artificial intelligence AI
Multi-agent-based sharing power economy for a smart communityD. Mahmood et al.
Int. J. Energy Res. (2017) © 2017 John Wiley & Sons, Ltd.
DOI: 10.1002/er
energy storage and transportation devices. Vehicle owners
and utility operator were major agents in the proposed test
system. In [31], a decentralized controller is proposed
using multi-agent technology to calculate optimal charging
time of EV battery, focusing user comfort in a demand
response program.
Table II. Nomenclature.
Smart home agents h
1
,h
2
,…,h
n
BESS agents for smart homes ESS
1
,ESS
2
,…,ESS
n
Smart meter agents SM
1
,SM
2
,…,
SM
n
Submeter agents SubM
1
,SubM
2
,…,
SubM
n
Billing agent smartBill PDH agent PDH
Unified-ESS agent smartESS On-peak price P
ON
Off-peak price P
OFF
Mid-peak price P
MID
PC of ksmart home Pc
k
PC of nsmart home Pc
n
Daily cost of one BESS PESS
k
BESS constant α
Storage capacity of ksmart home S
k
Storage capacity of nsmart homes S
n
Investment on BESS for ksmart home IESS
k
Investment on BESS for nsmart homes IESS
n
Holding cost of BESS P
WH
Unified-ESS BESS
1
+BESS
2
,…,
BESS
n
PC at P
ON
and P
MID
hours Pc
ON +MID
Daily expected cost of smart home ‘k’
Cd
k
PC at P
ON
Pc
ON
PC at P
MID
Pc
MID
PC at P
OFF
Pc
OFF
Daily PC of smart home ‘k’
Pcd
k
Capacity S(nk)ESS
n
-ESS
k
Charging of S
n
Scharging
n
Charging of S
k
Scharging
k
Discharging of S
n
Sdischarging
n
Discharging of S
k
Sdischarging
k
Power supply from utility PS
utility
Power supply from unified-ESS PS
unified ESS
Table III. State-of-the-art work: energy management solutions.
Domain Objective/feature Technique used Comments/highlights
Utility business
model [23]
Transformation of utilities
to service providers
Policy findings and
current limitations
Qualitative approach is used,
case-specific results
Futuristic approach of
PV generation [24]
Impact of retail rate design on
distributed PV deployment
Aggregate PV deployment
trends under net-metering
rules
Net-metering retail price
compensations have huge
impact on PV deployment
projection. New regulations
are needed to compensate
existing business models of
utilities.
Energy routing in
smart grid
network [25]
Energy routing with
minimum cost
Game theory Price decision strategy for
exchange of power between
futuristic micro-grids. Need to
use proposed scheme with
existing grid infrastructure.
DSM for
communities [26]
Minimize smart grid cost Non-cooperative game theory
using tensor product
Proposed system is regarding
remote communities with
networked grid.
DSM, appliance
clustering [27]
Autonomous PC regulation
to optimize cost
Prosumer-based DSM Investment costs neglected,
net metering is advocated
for a residual user.
Load management
using ESSs [28]
Optimal capacity and day-
ahead operation strategy
for ESSs
MATLAB interior-point
algorithm
Suitable for large-scale systems,
low adaptability regarding
prediction errors.
Multi-agent-based sharing power economy for a smart community D. Mahmood et al.
Int. J. Energy Res. (2017) © 2017 John Wiley & Sons, Ltd.
DOI: 10.1002/er
One major concern in using BESSs is the switching
mode. Langorse et al. [32] proposed such a switching
mechanism between multiple power sources including
storage devices of a micro-grid. Authors used multi-agent
paradigm with fuzzy rules to develop a control mechanism,
focusing state of charge (SoC) of storage devices. Taking
the work further ahead, McArthur et al. [33] developed a
state machine that is able to react over the power demand
and generation changes within the environment. An
extensive review regarding the use of MAS in power
domain is given in [34,35]. Table IV presents state-of-
the-art literature reflecting the role of MASs in smart grid
and energy management sector.
2.2. Motivation and problem statement
This work is motivated by Wang et al. [5], where they
presented the idea of joint ownership of ESSs among
electricity users and the local utility. They demonstrated
vast benefits that can be achieved by using such sharing.
However, battery sharing limits were not defined explicitly
among utility and users [5]. Within that facility, each user
has his or her own decision to take part in ESS sharing
scheme or not. Taking the work further ahead, authors in
[40] proposed an auction-based mechanism regarding
power use from joint ESS. Moreover, Mahmood et al.
[40] give a model of a competitive market for shared
ESS, whereas sharing economy is not based on the
competitive market. To attract end users, the concept of
power sharing with no profit, no loss approach is more
appealing that results in reduction of electricity bills with
minimal investment. Keeping the state-of-the-art work in
view, such a model is a need of time that ensures individual
cost reduction with minimum investment (cheaper
solution).
Hence it is required to develop software platforms that
ensure sharing power economy among self-motivated
users of a smart community and hardware platforms for
actual power distribution accordingly. Another challenge
is the scalability as communities tend to expand. Solution
needs to be highly scalable, which is able to accommodate
increasing number of participants in community.
Moreover, the scope of sharing economy for the power
sector, where users act as providers and takers
simultaneously, without involving market models is yet
to be explored.
Basic goals are (i) saving power, (ii) saving cost, and
(iii) saving environment, focusing on the power sector
[41]. In addition, DSM strategies were introduced that give
birth to numerous demand response programs [42].
However, owing to load shifting mechanisms or other
DSM approaches, user comfort was compromised as
reported in [43]. To solve user comfort and many other
problems, integration of renewable power generation
sources with smart grid was introduced and research is
ongoing. As an incentive for end users, the concept of
net metering evolved, which stated that extra produced
power can be sold back to the utility on some agreed
pricing model [44,45]. Net metering gave fresh breath to
utilities, but if bulk of power is produced locally, then it
seems to be against the business model of utilities, and
reluctance is reported in purchasing electricity back from
the demand side [[24], [46], [47]]. Considering the state-
of-the-art work, three major concerns are noticed that tend
to reduce the implementation of work carried out in the
research arena, that is,
•high initial investment,
•reluctance in net metering, and
•ownership of alternate energy resource (AER).
Existing techniques offer optimum analytical results;
however, their investment cost is one hurdle in
implementing that solution on a wide scale. Moreover, if
AERs are planted and they produce excessive energy,
utilities are reported to be reluctant in purchasing that bulk
Table IV. State-of-the-art work: MAS.
Domain Objective/feature Technique used Comments/highlights
Power distribution
congestion
control [30]
User and utility comfort-based
charging discharging of EVs
MAS using JACK,
MATLAB, Simulink
Cost sensitivity of end users is
considered, lowered computational
burden for distributed service operator.
Distributed energy
management [36]
Reduce engineering efforts
and cost
Consensus-based
distributed optimization
algorithms/MAS
Uncertainties in power generation and
demand fluctuate the obtained results.
More robustness is required.
Building EMS
optimization [37]
Reduction of complexities Optimal decision making
using MAS
Proposed solution has a potential risk of
not converging to an optimal decision
within decision-making time.
Micro-grid energy
management [38]
Efficient distributed
architecture for
energy management
Multi-agent with
non-cooperative
game theory
ESS capacity is not included in
performance analysis of this study
(dual natured appliance- power
consumer as well as distributor).
Hierarchical energy
management for
micro-grids [39]
Strategy for economic and
ecological benefits using
micro-grid
MAS-based multi-objective
optimization (particle swarm
optimization)
Proposed hybrid probabilistic forecast
approach has higher forecasting
accuracy.
Multi-agent-based sharing power economy for a smart communityD. Mahmood et al.
Int. J. Energy Res. (2017) © 2017 John Wiley & Sons, Ltd.
DOI: 10.1002/er
of surplus energy as it is against their business model, and
finally, only one entity owns the AER.
2.3. Contributions
The answer to the aforementioned problems lies in sharing
economy within the power sector. In this context, a sharing
power economy model is proposed on the foundation of
shared BESS. In this model, each user is offered more
flexibility in power usage pattern along with bill
reduction. A major objective of the proposed system is
to eliminate the purchase of electricity from utility at
on-peak price hours. To deal with scalability, reliability,
and autonomous decision-making issues, multi-agent
paradigm is utilized in this paper. The distributed nature
of MASs ensures optimum computational efficiency so
that overall computational load is distributed among a
number of agents [50].
Every home that tends to take part in the sharing power
economy has to invest in a BESS at the individual level.
Once BESS is owned by that individual home, then, using
MAS, locally administrated power distribution hub (PDH)
is proposed, which is capable of the following:
1. Using power from individual BESS of each smart
home.
2. Decision making (whether to use power from BESS or
utility).
3. Submeter incrementing or decrementing on power use
or share.
4. Smart billing of each smart home on PC and power
supply accordingly.
5. Flexibility in adding more smart homes within the
community.
6. PDH is a neutral entity. No profit, no loss approach.
For the benefits of the proposed PDH regarding cost
and energy savings to be validated, a comparative analysis
is conducted considering baseline PC without BESS, PC
with stand-alone BESSs, and proposed PDH (shared
BESS) mechanisms. Results show cost savings and
reduction of load at on-peak hours.
2.3.1. Paper organization
The rest of the paper is organized as follows: system
model is discussed in Section 3, which formulates PC
model and effective selection of stand-alone, small-scale
BESS for a residential unit. In Section 3.3, PDH is
modeled, which brings in sharing power economy aspect
in the power sector for a community or residential
compound. Section 4 reflects proposed MAS design and
basic framework. Section 4.1 demonstrates role of stand-
alone BESS (for individual home) in community energy
management by using MAS. In Section 4.2, MAS
anticipating PDH is presented. Section 5 gives numerical
results, analysis, and limitations of proposed work, while
the conclusion is presented in Section 6.
3. SYSTEM MODEL
In this section, initially, a baseline PC model is presented,
which is extended, integrating BESS to each smart home.
The impact of integration of BESS on overall smart
community considering PC and billing is studied in
forthcoming sections. Further extending the stand-alone
BESS model, we formulated the proposed shared BESS
(PDH) model to ensure optimal sharing of electric power
among the residents of the smart community.
3.1. Baseline power consumption model
Within a residential compound or society, there are nsmart
homes. The electricity tariff has three normalized stages,
that is, price on on-peak (P
ON
) hours, price on off-peak
(P
OFF
) hours, and price on mid-peak (P
MID
) hours, as
illustrated in Figure 1. It is desired that no or minimum
electricity is purchased from utility at P
ON
and P
MID
hours.
Baseline PC model presented in this paper is inspired
from [47]. Electricity consumption during P
ON
and P
MID
hours is taken into account. These hours are crucial with
higher probability of PC peak generation. Out of nsmart
homes, a smart home (k) is studied to analyze its PC
initially. PC is a random process that varies every day for
every user. Hence, there are three random processes of
PC for 24 h: Pc
ON
(t), Pc
OFF
(t), and Pc
MID
(t). It can be
stated that PC of a smart home ‘k’for 24 h of a day is
Pc
k
=Pc
ON
(t)+Pc
OFF
(t)+Pc
MID
(t), where tis the duration
in hours for on-peak, off-peak, and mid-peak prices. As
these are random processes, CDF can be stated as
FPcON tðÞ :ðÞ,FPcMID tðÞ :ðÞ, and FPcOFF tðÞ :ðÞ, while probability
density function can be stated as ∫PcON tðÞ :
ðÞ,∫PcMID tðÞ :
ðÞ,
and ∫PcOFF tðÞ :ðÞ, respectively. CDF of kfor a day considering
Pc
ON
(t), Pc
MID
(t), and Pc
OFF
(t) can be stated as in
Equation (1).
FPck:ðÞ¼FPcON tðÞ :ðÞþFPcMID tðÞ :ðÞþFPcOFF tðÞ :ðÞ (1)
The PC of a home differs from that of another; in
addition, the average CDF of a single home (using
Equation (2)) is taken, which computes collective energy
Figure 1. Pricing mechanism. [Colour figure can be viewed at
wileyonlinelibrary.com]
Multi-agent-based sharing power economy for a smart community D. Mahmood et al.
Int. J. Energy Res. (2017) © 2017 John Wiley & Sons, Ltd.
DOI: 10.1002/er
consumption of nsmart homes, as can be seen in
Equation (3).
FPck:ðÞ→
1
n∑
n
k¼1
FPcktðÞ :ðÞ
(2)
The PC of nhomes can be stated as
Pcn¼∑
n
1
FPck:ðÞ
n
(3)
3.1.1. Battery energy storage system selection and
investment
In meeting power demands economically, BESS is a
promising and affordable solution. Considering three
levels of any pricing mechanism, investment on storage
device is feasible if estimated power usage at peak and
mid-level price hours is greater than that at off-peak
pricing hours. In this work, single power source is in focus;
that is, utility, and BESSs are charged during off-peak
hours. If PC
ON +MID
<PC
OFF
, then installing a BESS
may not be much beneficial to end users. Let PESS
kbe the
daily capital cost of any chosen storage device for a
residential unit k. As stated earlier, the lifetime of a BESS
is minimum of 15 years.
If Equation (4) is true,
PcON þPcMID
PcOFF >0 (4)
then it is feasible to invest on a storage device [48]. A
BESS constant αis defined, as expressed in Equation (5)
whose value must rest between 0 and 1.
α¼PON :PcON þPMID:PcMID
POFF :PCOFF
PESS
PON :PcON þPMID:PcMID
POFF :PCOFF
(5)
Investment on a storage device is feasible only if
P
ESS
<(P
ON
.Pc
ON
+P
MID
.Pc
MID
)P
OFF
.PC
OFF
and
0<α<1 [48]. It is assumed that all nsmart homes fulfill
this condition. Charging of BESS is carried out only
at P
OFF
times, while the storage cost in terms of losses
is P
OFF
.KWpH. Holding charge cost per day is
represented as P
WH
. Investment on BESS of k(single
smart home) is represented as IESS
kand IESS
nfor nsmart
homes. Storage capacity of a BESS is S
k
and S
n
,
respectively, for a smart home kand nhomes within
a community. To calculate optimum capacity of a
storage device for a smart home (S
k
), Equation (6) can
be used. Optimal selection of BESS (regarding energy
storage capacity) is achieved by obtaining an average
of last 30 days’consumed power during on-peak and
mid-peak pricing hours.
Sk¼1
30 ∑
30
k¼1
PcON
kþPcMID
k
(6)
3.2. Stand-alone battery energy storage
system model
Smart home kconsumes S
k
amount of electricity at
P
ON
+P
MID
hours, and if S
k
falls short, kbuys electricity
from the smart grid at the utility price of that hour. khas
to pay for consumed electricity as
PckPcOFF
k
þSk
PonjPMID
(whichever utility
price of the time is). Daily expected cost of kis referred
as Cd
k. Equation (7) gives the objective function regarding
expected daily cost of a stand-alone smart home kwith a
storage device.
Cd
k
¼argmin Cd
k
(7)
such that
Scharging
k→POFF a(8)
Sdischarging
k→PON and PMIDb(9)
Schargeleft
k≤80%and Thours ¼PON ∣PMID;PCk→PSutilityc
(10)
Constraints ‘a’and ‘b’state that charging of BESS must
be performed at P
OFF
hours while discharging at P
ON
or
P
MID
hours. On the other hand, constraint ‘c’enforces
switching of power source from BESS to utility if charge
left of BESS is less than or equal to 20%. The capacity
of installed BESS is made such that it can bear load at
P
ON
timings as well as P
MID
timings; however, there is a
possibility that load consumption of kincreases or
decreases as PC is a random process. Here, three
possibilities can occur: PcON þMID
k>Sk>PcMID
k,Sk>
PcON
kþPcMID
k,or Sk¼PcON
kþPcMID
k. Daily expected
cost of kcan be stated as in following conditional
statement.
Multi-agent-based sharing power economy for a smart communityD. Mahmood et al.
Int. J. Energy Res. (2017) © 2017 John Wiley & Sons, Ltd.
DOI: 10.1002/er
where
P
WH
and P
ESS
are holding cost and capital daily cost of
BESS, respectively; POFF
kSkþPcOFF
k
reflects
electricity cost at off-peak price hours including charging
cost of batteries along with PC of normal appliances; and
PON
kPcON Sk
ðÞ
þ
hi
represents that the result is a
positive value.
3.3. Shared battery energy storage systems
(power distribution hub) model: within a
smart community
Consider a smart community of msmart homes and out of
which nagrees to participate in sharing economy for
combined BESS. Every residential unit is equipped with
its own energy management system, and appliances are
scheduled accordingly. The PC pattern of every residential
unit varies, and there is a probability of purchasing power
at on-peak price hours, when the storage is fully drained.
The proposed PDH combines BESS of nsmart homes.
These nsmall-scale BESS are linked together, forming a
unified-ESS for the smart community, and each residential
unit can use power from unified-ESS considering different
constraints. This PDH is a neutral authority, locally
governed on ‘no profit, no loss basis’, and it does not
support any market model. Block diagram of proposed
system model is presented in Figure 2.
As illustrated in Figure 2, nsmart homes are connected
with a PDH. This PDH has a connection from the utility
and has a smart meter and authority on the collective
power storage system (unified-ESS). Utility power
distribution system (PDS) reaches PDH, and a private
PDS distributes power in community. Excessive energy
is not sold back to the smart grid as in net metering but
is used within the community. Distribution of power
among all users is behind the smart meter. For billing,
submeters are installed at each home. These submeters
increment or decrement on power use and power share,
respectively.
The PC pattern of each smart home is unique; however,
there is no big difference in their power demand as these
homes are homogenous in nature (contains an almost
similar set of electrical appliances). Every
home/residential unit installs BESS according to the needs
and may result in a variety of BESSs for nsmart homes.
The following assumptions are made for power system
model.
•BESS capacity selection decision is made based on
unique power demands of home.
•Homes within a smart community do not have huge
differences in PC patterns.
•Collection of nsmart homes invests differently on
respective BESSs.
•Surplus energy is stored or shared but not sold to utility
company—no net metering.
•BESS complete one cycle of charging and discharging
in a day.
•Fixed holding costs for all BESS.
•Lifetime of any installed BESS is 15 years minimum
[49,50].
•PDH is responsible of distributing energy to residential
units, either from utility or from unified-ESS.
Figure 2. Block diagram of proposed model; PDH.
Cd
k¼
PWH PESS þPOFF
kSkþPcOFF
k
þPON
kPcON Sk
þPMID
kPcMID
kSk
if PcON þMID
k>Sk>PcMID
k
PWH PESS þPOFF
kSkþPcOFF
k
if Sk>PcON
kþPcMID
k
PWH PESS þPOFF
kSkþPcOFF
k
þPON
kPcON Sk
ðÞ
þ
hi
þPMID
kPcMID
kSk
if Sk¼PcON
kþPcMID
k
8
>
>
<
>
>
:(11)
Multi-agent-based sharing power economy for a smart community D. Mahmood et al.
Int. J. Energy Res. (2017) © 2017 John Wiley & Sons, Ltd.
DOI: 10.1002/er
A PDH is composed of a combination of energy
storage devices owned by individual smart homes, energy
routing block, and billing block. Figure 3 illustrates a
flow diagram of energy storing, routing, and billing for
each home within the residential compound or
community.
Objective function regarding expected daily cost of n
smart homes of a community is represented as in
Equation (12).
Cd
n
¼argmin Cd
n
(12)
such that
Scharging
n→POFF a(13)
Sdischarging
n→PON and PMIDb(14)
Schargeleft
k≤80%and Thours ¼PON ∣PMID;PCk→PSunifiedESS c
(15)
Schargeleft
n≥80%;PCk→PSutility d(16)
Constraint ‘a’states that charging of unified-ESS must
be performed at P
OFF
hours. Constraint ‘b’reflects the
discharging hours of unified-ESS, that is, P
ON
and P
MID
hours. If charge on S
k
reaches level 0 (20% charge left
of total BESS capacity) during high-peak or mid-peak
pricing hours, power is used from unified-ESS until
capacity of unified-ESS reaches level 0 as depicted by
constraint ‘c’. On the other hand, constraint ‘d’elaborates
that if charge on unified-ESS reaches level 0, power is
consumed from utility by individual smart homes. In this
work, level 0 reflects 80% depth of discharge, level 5is
90% depth of discharge, and level 10 is 100% discharge
of BESS.
Each home has its own power requirement and
criteria. According to these requirements, users invest
in BESS. This results in a variety of devices (to store
electric power). BESSs of all homes are placed (can be
placed physically or logically) in PDH, from where a
unified-ESS is formed having a storage capacity of
S
n
=(S
1
,S
2
,S
3
,…,S
n
), while holding charge cost of n
BESSs (unified-ESS) is expressed as PWH
n. As explained
earlier, BESS is charged only in P
OFF
hours. If k
demands power even after consuming S
k
, then PDH
provides power from unified-ESS (S
(nk)
), given that
there is excessive power stored in smartESS. Hence,
any smart home that demands extra power at P
ON
or
P
MID
hours is provided by PDH at the rate of P
OFF
.
Figure 3. PDH: local energy routing and billing flow chart.
Multi-agent-based sharing power economy for a smart communityD. Mahmood et al.
Int. J. Energy Res. (2017) © 2017 John Wiley & Sons, Ltd.
DOI: 10.1002/er
Expected PC of all residential units can be referred as in
Equation (12). Daily expected cost using unified-ESS of
nhomes is expressed as
It is an ideal condition, if Sn¼Pc ON þMIDðÞ
nand all
homes pay bills at the rate of P
OFF
and objective of
lowering cost and not purchasing electricity at P
ON
times
is achieved. However, there can be two cases: case 1 is
expressed in Equation (25) as
Sn¼∑
n
k¼1
Sk;SnkðÞ
:PcON
kþPcMID
k
þ
(25)
Equation (25) (case 1) depicts that all the residential
units enjoy P
OFF
pricing and some charge is still left in
unified-ESS that can be utilized for community welfare
(parking lights, etc.). Considering case 2 (Equation (26)),
if total storage capacity fails to meet power demand at
on-peak and mid-peak price hours, then smart homes have
to purchase electricity from utility at utility price.
Sn¼∑
n
k¼1
PcON
kþPcMID
k
Sk;SnkðÞ
Þþ(26)
The + sign at the end of equation (25) and (26) reflects
that the answer of these equations is a positive value.
4. MULTI-AGENT SYSTEM DESIGN
In this work, two MASs are developed adhering to the
Foundation for Intelligent Physical Agents (FIPA)
standards. FIPA aids in developing agent-based systems
by setting up software standards. These software standards
ensure inter-operability between multiple MASs at some
level. Initially, a MAS is developed for a smart community
where BESSs are installed individually in accordance with
the framework presented in Section 3.2. Power is stored at
P
OFF
hours. This stored power is utilized during P
ON
and
P
MID
hours to limit electricity bills.
In the second approach, a MAS is developed, based on
proposed framework presented in Section 3.3. Individual
small-scale BESSs of residential units (which are willing
to take part in sharing power economy) are joined together
forming a unified-ESS. Charging of unified-ESS is
performed at P
OFF
hours from utility, whereas power from
unified-ESS is used at P
ON
and P
MID
hours. PC is a variable
phenomenon. If a smart home consumes more power than
its stored power, it obtains it from unified-ESS. This extra
consumption form unified-ESS ubiquitously increments
submeter of respective home at the rate of P
OFF
.
In the following sections, agent mapping and
communication regarding both MASs are explained
(Figures 4 and 5).
4.1. Multi-agent system design: stand-alone
battery energy storage system model
4.1.1. Agent mapping
1. Smart home agents (model-based agents) {h
1
,h
2
,
…,h
n
}.
2. BESS agents (reactive agents) for the respective smart
homes {ESS
1
,ESS
2
,…,ESS
n
}.
3. Smart meter agents (reactive agents) for respective
smart homes {SM
1
,SM
2
,…,SM
n
}.
4.1.2. Agent communication
It is assumed that h
k
contains the schedule and per hour
price signal of the next 24 h for k. This schedule and price
signal is communicated to ESS
k
agent whose major tasks
are to
•maintain SoC and
•communicate SoC to h
k
.
h
k
has an optimum PC schedule for next 24 h based on the
price signal. ESS
k
is charged at low-priced hours as
decided by h
k
. For longer power storage life, storage
device must not be discharged completely [48,49]; hence
level 0 (80% discharge of batteries) is set for each BESS.
When SoC reaches level 0, ESS
k
transmits a warning
message to h
k
advocating switching of PC from ESS
k
to
the utility. If h
k
does not respond to this message owing
Cd
n¼
∑
n
k¼1
PWH PESS þPOFF
kSkþPcOFF
k
þPON
kPcON Sk;SnkðÞ
þPMID
kPcMID
kSnkðÞ
if PcON þMID
k>Sk>PcMID
k
∑
n
k¼1
PWH PESS þPOFF
kSkþPcOFF
k
;POFF
kSk
½
if Sk>PcON
kþPcMID
k
∑
n
k¼1
PWH PESS þPOFF
kSkþPcOFF
k
þPON
kPc ONðÞSkÞþ
i
þPMID
kPcMID
kSk
h
if Sn¼PcON
nþPcMID
n
8
>
>
>
>
>
>
>
>
>
>
<
>
>
>
>
>
>
>
>
>
>
:(24)
Multi-agent-based sharing power economy for a smart community D. Mahmood et al.
Int. J. Energy Res. (2017) © 2017 John Wiley & Sons, Ltd.
DOI: 10.1002/er
to continuity of operations or any uncertainty, then at level
5 (90% discharge of BESS), ESS
k
automatically switches
power from BESS to the utility. Storage device is fully
drained at level 10 (100% discharge of BESS).
Figure 4 illustrates the proposed MAS without BESS
sharing.
4.2. Multi-agent system design: shared
battery energy storage systems (power
distribution hub) model
4.2.1. Agent mapping
•Smart home agents (model-based agents) {h
1
,h
2
,
…,h
n
}.
•BESS agents (reactive agents) for respective smart
homes {ESS
1
,ESS
2
,…,ESS
n
}.
•A smart ESS agent (goal-based agent) that takes care of
all individual BESS forming a unified-ESSS (smartESS).
•Submeter agents (increments or decrements on power
use or share (reactive agents)) for respective residential
units {SubM
1
,SubM
2
,…,SubM
n
}.
•Billing agent (reactive agent) that calculates bills by
studying respective submeters of each smart home
(smartBill).
•Power distribution agent (reactive agent) that ensures
smooth power distribution among community network.
Also, it receives messages from utility regarding power
network and has a single smart meter connected to the
utility (PDH).
In this scenario, smartESS takes care of nBESSs of
respective nresidential units that are joined together
forming a unified-ESS. Each respective ESS
k
communicates with a smartESS, informing its SoC, and
based on linear programming, smartESS decides which
ESS
x
is to be operated during on-peak or mid-peak hours.
4.2.2. Agent communication
Considering MAS presented in Figure 5, the PDH receives
electricity tariff for next 24 h. This price signal is distributed
among all smart home agents, that is, {h
1
,h
2
,…,h
n
}. We
assume that h
k
has received price signal and developed an
optimum PC schedule. This schedule is transmitted to
smartESS, which has already received the information
regarding SoC of BESSs, that is, {ESS
1
,ESS
2
,…,ESS
n
}.
Based on the PC schedule and price signal, smartESS decides
the charging and discharging of ESS
k
for the respective h
k
.At
P
ON
and P
MID
hours, stored energy by ESS
k
is utilized by h
k
agent. If stored power of ESS
k
≤level 0, smartESS allows h
k
to use power from unified-ESS (S
(nk)
). Before allowing it
to use power from shared ESS, a message is generated to
h
k
,SubM
k
, and smartBill, advertising the collapse of ESS
k
power. h
k
starts using power from ESS
j
portion of unified-
ESS. SubM
j
starts decrementing, while SubM
k
starts
incrementing on consumed power at the upper limit of the
P
OFF
range. The cost is slightly higher with respect to BESS
charging cost that gives a cushion for the power losses. If
smartESS reaches level 0, power is purchased from the utility
at utility price and the bill is calculated at individual submeter.
Figure 4. MAS architecture: stand-alone BESSs.
Multi-agent-based sharing power economy for a smart communityD. Mahmood et al.
Int. J. Energy Res. (2017) © 2017 John Wiley & Sons, Ltd.
DOI: 10.1002/er
5. NUMERICAL STUDIES AND
RESULTS
Numerical simulations are conducted anticipating three
different scenarios considering a smart community
comprising of ten homes. Experiments are performed using
Java Application Development Environment, and figures
are developed in MATLAB. These scenarios are as
follows:
1. Sc1—Baseline energy consumption and cost paid
without using BESS within a smart community
(Section 3.1).
2. Sc2—Smart community with stand-alone BESSs as
presented in Section 3.2 following MAS architecture
as discussed in Section 4.1.
3. Sc3—Community with shared BESS (unified-ESS)
based on the framework presented in Section 3.3
(reflecting sharing power economy and MAS design
presented in Section 4.2).
Considering Sc1, it is assumed that every residential
unit is equipped with energy management system and
proper schedules are made to shift load to low-priced
hours, without affecting much of the user comfort. There
is no BESS in Sc1 as it provides baseline for further
comparative analysis. In Sc2, smart homes are equipped
with BESSs at the personal level. With the help of the
BESS installed at each home, energy preservation and cost
reduction are demonstrated. Sc3 reflects the proposed
model (PDH), that it not only unifies individual BESSs
but also takes care of incrementing or decrementing
respective submeters considering power use or share from
unified-ESS (efficient billing), optimal charging and
discharging of storage devices and efficient decision
making on use of power source.
Price signal and storage capacity of BESS installed by
each home in community are depicted in Figure 6(a, b),
respectively. Real-time price signal is used in this work
whose low price value is 1.9 cents per kilowatt-hour,
mid-level pricing is 2.3 cents per kilowatt-hour, and high
price is set at 2.8 cents per kilowatt-hour. There are two
peaks in price curve as well, which are neglected in
formation of upper, mid, and lower price levels. Average
price including the peaks is 2.5 cents per kilowatt-hour as
can be seen in Figure 6(a).
5.1. Cost and load profiles of individual
smart homes
Figures 7 and 8 depict a comparison of without BESS
(Sc1), with BESS (Sc2), and with shared BESS (PDH)
Figure 5. MAS architecture: PDH. [Colour figure can be viewed at wileyonlinelibrary.com]
Multi-agent-based sharing power economy for a smart community D. Mahmood et al.
Int. J. Energy Res. (2017) © 2017 John Wiley & Sons, Ltd.
DOI: 10.1002/er
(Sc3) models at individual smart homes. Considering Sc1,
plots are achieved by simple multiplying load per hour at
price per hour for the ten smart homes. Anticipating Sc2,
smart meter agents ({SM
1
,SM
2
,…,SM
n
}) computes
respective electricity bills (Figure 7) based on the received
PC signal (Figure 8) from smart homes agents ({H
1
,H
2
,
…,H
n
}). For Sc3, smart bills are computed by smartBill
agent that received PC signal from submeter agents
({SubM
1
,SubM
2
,…,SubM
n
}).
Figure 7 represents cost profiles of ten smart homes,
while Figure 8 illustrates the purchased power from
utility, and it does not include consumption of power
from storage devices. Figure 7 depicts overall cost that
includes charging, sharing, or using stored charge costs
at time of use. Each graph represents the data of single
smart home. Considering Sc1, the cost paid and energy
consumption from the utility for 1 day are highest in
comparison with those of Sc2 and Sc3. With the
integration of individual BESS for each residential unit
(Sc2), consumption of power at P
ON
and P
MID
hours is
shifted on BESS until it reaches its maximum depth of
discharge, whereas if a smart home needs some power
even after consuming its BESS, the proposed PDH issues
its power requirement from unified-ESS following the
incrementing and decrementing of submeters as
discussed earlier.
Among these ten smart homes, the difference between
Sc2 and Sc3 lies in use of extra power needed after 80%
discharge of BESSs. In Sc3, the concept of unified-ESS
allows end users to use electricity at low price range, that
is, the charging prices. Hence, it lowers the electricity
bills and limits use of power at crucial hours. The range
of BESS charging rests between 1.9 and 2.3 cents per
kWh in this particular scenario. Submeter of sharing
smart home decrements at the rate of 2.2 cents per
kWh and consuming smart home’s submeter will
increment at the same rate. The cost is highest during
24 h of the day considering Sc1, especially at P
ON
and
P
MID
hours and same goes with load profiles, as energy
is purchased from the utility. Anticipating Sc2, where
BESSs are installed at the individual level, some houses
have to buy electricity from utility as can be seen in
graphs representing cost and load profiles (Figures 7
and 8). Sc3 represents the major contribution of this
work. In this scenario, the excessive cost paid at on-peak
hours is at the rate of off-peak hours (storage devices
were charged during off-peak hours). This results in
minimum cost to end users.
Table V explains per home PC and bills for one BESS
cycle. It can be seen that unified-ESS (Sc3) supersedes
stand-alone BESS (Sc2) mechanism in terms of cost and
energy savings. At low-priced hours, power is consumed
more with respect to Sc1, as energy is stored from the
utility at P
OFF
hours, which is used at P
ON
and P
MID
hours
to limit electricity bills. Regarding Sc3, power sharing or
power consuming from BESS decrements or increments
the submeter; hence load on utility and cost vary
dynamically.
5.2. Cost and load profiles of a smart
community
In this section, the impact of the proposed PDH framework
(Sc3) is compared with that of Sc1 and Sc2 considering the
whole community. Figure 9(a, b) illustrates per hour
electricity consumption cost and PC, respectively. In
analyzing results (expressed in Figure 9(b)), the overall
cost of electricity used by a smart community considering
Sc1, Sc2, and Sc3 is 2304.30, 1568.6, and 1441.30 cents,
respectively. Using BESS (as in Sc2) gives a vital impact
on cost (31% cost saving). The proposed concept of
sharing power economy (i.e. PDH) within a community
gives saving of 36% with respect to Sc1 and 9% with
respect to Sc2. Focusing smart homes at the individual
level, savings in Sc3 with respect to Sc2 are within a range
of 6% to 12%. On the other hand, comparing PC from
utility, smart community used 916, 758, and 715 kWh
Figure 6. Price signal and BESS capacity. (a) Price per hour and
(b) BESS capacity. [Colour figure can be viewed at
wileyonlinelibrary.com]
Multi-agent-based sharing power economy for a smart communityD. Mahmood et al.
Int. J. Energy Res. (2017) © 2017 John Wiley & Sons, Ltd.
DOI: 10.1002/er
Figure 7. Cost profiles of ten smart homes. [Colour figure can be viewed at wileyonlinelibrary.com]
Figure 8. Load profiles of ten smart homes. [Colour figure can be viewed at wileyonlinelibrary.com]
Multi-agent-based sharing power economy for a smart community D. Mahmood et al.
Int. J. Energy Res. (2017) © 2017 John Wiley & Sons, Ltd.
DOI: 10.1002/er
for Sc1, Sc2, and Sc3, respectively. Anticipating smart
homes individual load consumption profiles (Figure 8),
BESS charging load increases at low-priced hours and
during high and mid pricing hours. Sc3 makes sure that
during P
ON +MID
hours, electricity must be used by
unified-ESS, if individual BESS charge left is less than
20%. The proposed PDH (Sc3) saves 21% and 6% of
utility power with respect to Sc1 and Sc2, respectively.
Figure 10(a, b) demonstrates PC of a smart community.
Peaks are generated at low-priced hours as BESSs are
charged during off-peak hours and power is not purchased
from the utility at on-peak hours as can be seen in
Figure 10(a). That is the reason that load on utility
considering Sc4 is null. Considering Sc2, some power is
purchased at P
ON
or P
MID
hours from the utility.
Nonetheless, its performance is better with respect to
Sc1; however, Sc3 outperforms Sc2 as well by not
purchasing power during mid or high pricing hours.
Life of power storage increases with care. Storage
devices should not be drained out completely; hence this
is the reason that a threshold value is set. According to
MAS design, at level 0, a soft warning is issued to switch
power source, and at level 5, system shifts the power
source (other storage device or utility). This increases the
lifetime of batteries [48,49].
5.3. Limitations and future directions
Battery energy storage systems have vast potential to
optimize energy consumption resourcefully, which in turn
not only reduce electricity bills at the individual level but
collectively take an active part in preserving atmosphere
(limiting carbon emissions) and natural resources (usage
of fossil fuels) as well. It is advocated to install a BESS with
respect to the power consumed during on-peak and mid-
peak hours of the day as discussed earlier. It is assumed that
the only power source available for a smart community is
the utility. There is no renewable energy generation plant
or small-scale micro-grid available. Investment costs are
not investigated and analyzed in this work. Later on, these
stand-alone BESSs are joined together, forming a unified-
ESS that is shared among the whole smart community. This
study has certain shortcomings, as holding cost is kept
Table V. PC and bill of each smart home within smart community.
Smart homes
Without BESS Stand-alone BESS Unified-ESS
Power used (kWh) Cost paid (cents) Power used (kWh) Cost paid (cents) Power used (kWh) Cost paid (cents)
Home 1 85 210.10 76 156.90 72 141.30
Home 2 96 241.20 77 158.60 75 153.80
Home 3 94 243.60 84 172.00 81 161.80
Home 4 79 197.70 72 150.20 68 138.00
Home 5 116 294.90 79 165.10 73 142.50
Home 6 85 210.10 79 164.30 74 148.90
Home 7 85 213.70 62 127.20 58 117.60
Home 8 93 242.60 80 166.30 74 151.70
Home 9 97 240.30 80 166.60 72 147.40
Home 10 85 210.10 69 141.40 68 138.30
Community 916 2304.30 758 1568.6 715 1441.30
Figure 9. Smart community cost profile. (a) Per hour cost
comparison of smart community. (b) Bill comparison: smart
homes of smart community (in cents). [Colour figure can be
viewed at wileyonlinelibrary.com]
Multi-agent-based sharing power economy for a smart communityD. Mahmood et al.
Int. J. Energy Res. (2017) © 2017 John Wiley & Sons, Ltd.
DOI: 10.1002/er
constant whereas every BESS has its own parameters
regarding holding costs. Cost due to power losses within
private/community PDS is not precisely taken into account,
and it needs to be optimized.
Battery energy storage system has certain disadvantages
as well. Although its initial costs are lower than investing
on a small-scale renewable energy source, its maintenance
and installing costs are high. This can be a limitation in
expanding the proposed system on a wide scale. However,
with the advancements in power storage systems with
respect to prices, these systems may reach affordable range
in the near future. In this work, based on previous PC
history of 30 days, optimum selection regarding capacity
of BESS is provided; however, it needs to be more accurate
and precise and will be dealt with in future works.
Moreover, certain electrical appliances such as EVs that
can influence hugely on energy management are not
considered in this work. Optimal configuration of BESS
for a smart home, and energy management using EVs
along with integration of renewable sources and a
complete energy management architecture of a smart
community based on the aforementioned shortcomings, is
the focus in our future works.
6. CONCLUSION
Battery energy storage systems have vast potential in
energy preservation and bill reduction. In this work, major
emphasis is on sharing power economy of small-scale
storage systems and their impact considering cost
reduction and PC considering a smart community using a
real-time pricing signal.
A multi-agent-based PDH is proposed that monitors and
controls individual BESSs of respective smart homes
within a smart community. Proposed framework automates
multiple BESS processes to achieve financial benefits to all
users. The developed system is scalable, adaptable, and
extendable as communities tend to expand with passage
of time. Developed MAS is intelligent enough to make a
decision whether to consume power from utility power or
BESS and generate bills accordingly. Finally, numerical
simulations were conducted that ensures cost and energy
benefits of using the proposed PDH under the concept of
sharing power economy. Moreover, using proposed
sharing power economy mechanism, system’s operating
cost and individual electricity bills are reduced. For the
impact of proposed mechanism to be found, three
scenarios are modeled, that is, without BESSs, with
BESSs, and with sharing power economy of BESSs
(PDH). Twenty-one percent and 6% power savings are
achieved with respect to baseline and without sharing
ESS consumption from utility. Anticipating bill reduction,
36% of bill is reduced if compared with baseline cost and
9% with respect to without sharing of BESS. Reduction
in PC gives breath to utilities in modifying their generation
and distribution systems.
On the other hand, if overall PC of the smart
community is analyzed, charging of BESSs at off-peak
hours generates PC peaks, whereas peaks at any time of
day are disruptive for grid infrastructure. There is a need
to formulate a highly scalable and adoptable scheduling
mechanism for charging of BESSs. In this work, only
one power source, that is, grid or utility, is taken into
account without considering other distributed energy
resources. There is a need to incorporate renewable energy
sources and micro-grids with sharing power economy
concept to investigate policy implications and benefits of
distributed system operations, aiming for social welfare.
ACKNOWLEDGEMENT
The authors extend their appreciation to the International
Scientific Partnership Program ISPP at King Saud
University for funding this research work through
ISPP 0053.
Figure 10. Smart community load profile. (a) Per hour load
comparison of smart community. (b) PC comparison: smart
homes of smart community (in kWh). [Colour figure can be
viewed at wileyonlinelibrary.com]
Multi-agent-based sharing power economy for a smart community D. Mahmood et al.
Int. J. Energy Res. (2017) © 2017 John Wiley & Sons, Ltd.
DOI: 10.1002/er
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