A home-to-home energy sharing process for domestic
peak load management
Khizir Mahmud†, M.S.H. Nizami*
†
School of Electrical Engineering and Telecommunications
University of New South Wales, NSW 2052, Australia
*
School of Engineering, Macquarie University,
NSW 2109, Australia
khizir.mahmud@unsw.edu.au
Abstract—The increase utilization of various intermittent
renewable energy sources and the new types of mobile loads
necessitates the implementation of a domestic energy management
system. This energy management scheme provides an option to
maintain the household peaks at a specific range, and at the same
time provides various ancillary services. In this paper, we propose
a technique to curtail the peaks of the domestic power demand and
share the excess energy with the neighbours in need. The method
utilizes photovoltaics (PVs), electric vehicle, and battery storage at
the domestic point and manages them based on some predefined
algorithms. The proposed method is tested in a real Australian
power distribution network and has proved to minimize the
domestic peak load demand of the owner and their neighbour
substantially, hence expected to reduce the energy cost.
Keywords—energy management; energy sharing; neighborhood
energy exchange; peer-to-peer energy trading; power sharing.
I. INTRODUCTION
The future smart cities and the internet-of-energy (IoE) will
utilize the plug-and-play facility to connect portable energy
sources and loads [1]. At the same time, the traditional energy
sources will be replaced by both large and small-scale renewable
energy sources, with the conventional loads still in existence.
Some loads will also work as a source at the same time, e.g.,
portable battery bank and electric vehicle (EV). Due to this
advantage, the electricity users will not only function as a
unidirectional consumer but also as a seller, alternatively known
as prosumers. Prosumers will have a facility to sell any excess
energy from their energy resources such as photovoltaics (PVs),
battery storage, and EVs [2]–[5]. Various intelligent
management techniques will coordinate with the source and load
conditions to optimize the energy flow from/to sources/loads
(controlled energy storage charging and discharging) and
maximize the energy savings [6]–[8]. Additionally, the impact
of the small-scale intermittent PV power generation [9] at the
domestic bus, or large-scale aggregated PVs at the substation
level can also be minimized with an intelligent control technique
using battery storage and EVs [6]–[8].
The process of peer-to-peer information transfer is expected
to be familiar in case of energy transfer [1]. In case of the energy
transfer, prosumers will be able to utilize their energy sources
best and make the profit without the involvement of any third
party. If appropriately managed, this could potentially reduce the
M. J. Hossain*, Jayashri Ravishankar†
*
School of Engineering, Macquarie University,
NSW 2109, Australia
†
School of Electrical Engineering and Telecommunications,
University of New South Wales, NSW 2052, Australia
intermittency problem of the renewable energy sources and the
mobility concerns of the loads [1]. Moreover, the need for
infrastructure developed for the excess demand can be reduced
if the local energy transactions are managed intelligently. Since
the peak load occurs for a short period, this peer-to-peer energy
transfer can also flatten the load curve through a smart energy
sharing and management process.
Most of the energy management deals with either energy
resource management [10], [11] or load management [2], [12]
techniques. Energy resource management techniques mostly
deals with the energy resource (PV, EV, battery) scheduling. On
the other hand, load management techniques use the load
shifting strategy [2], [12]. Price-based [3], load demand-based
[4], [5], or a multi-agent system (MAS)-based [13] demand
management using EVs [4], [5], or battery storage along with
renewable energy sources [14] are also commonly used strategy.
Some authors have discussed the economic benefit of the power
sharing in microgrids [15], [16]. Reference [17] demonstrated
the day ahead appliance scheduling among neighborhood to
reduce the energy cost. Energy hub strategies based on the
decentralized energy systems integration method are
investigated to reduce the energy demand peaks in the
neighborhood and minimize overall energy consumption [18]–
[20]. However, there has been a little study about the peak load
management and the utilization of any excess energy through
neighborhood energy sharing process.
In this research, an algorithm is developed to reduce the
domestic peak load demand using EV, PV, and battery storage
in real-time. Any excess energy after meeting the owner’s need
is shared with the neighbor in a peer-to-peer energy transfer
process, where the involvement of any third party is eliminated.
The main advantage of this algorithm is that it considers peak
loads in energy sharing and energy management process, and
tries to minimize it.
II. SYSTEM OVERVIEW
In the proposed system, power sharing between two houses
are considered. It is assumed that one house is equipped with
renewable energy sources and storage with energy management
system. This home is denoted as ‘parent’. Another house is a
regular house without any renewable energy sources or storages
and fully depend on the power from the grid. This house is
978-1-5386-5186-5/18/$31.00 ©2018 IEEE
denoted as the ‘child’. An energy management algorithm is
developed to minimize the peak load demand and share energy
by coordinating between PV, battery storage, and EV based on
the load condition, state-of-charge (SOC), and chargingdischarging constraints of the storages.
House- 1
Battery
Conv-2
η2
III. PROPOSED ALGORITHM
+
EV
This section describes the proposed control algorithm for
neighborhood power-sharing from the excess energy of peak
load management. Firstly, it discusses the peak load
management of parent (who will share power from its energy
storages), and then explains the power sharing process to the
child (who requires energy) from the excess energy of parent.
The load conditions are classified into two states, i.e., off-peak
and peak period. Let assume the total time of a day (24 hours) is
divided into peak and off-peak periods.
PV
Conv-1
Conv-4
Conv-3
Controller
η1
η5
AC BUS (House- 1)
Conv-5
η3
η4
AC Load
=
η6
AC BUS (House- 2)
House- 2
(1)
∪
Here,
is the period of peak load, and
is the base load
and
are multiplied with the peak
(off-peak) period. Both
and off-peak load occurrence frequency
, because peak and
off-peak load may occur several times in a day. The domestic
load curve of the parent is expressed as:
Load
Conv-6
Power Grid
energy from PV is directly bypassed to the battery to charge
during off-peak hours. The battery is connected to a bidirectional
dc-ac/ac-dc converter (conv-3). The EV is connected through a
vehicle-to-grid (V2G)-enabled charger (conv-4). Converters 5
and 6 are used to control the flow of the power and operated by
the controller based on the parent’s and child’s power
availability and demand, respectively.
Fig.1. An overview of the proposed system.
In the parent, all the energy sources, i.e., PV, battery, EV, and
the loads are connected to a universal bus. One point of PV is
connected to the bus through a unidirectional dc-dc converter
(conv-1), and another point is connected to the battery through
another unidirectional dc-dc converter (conv-2). The excess
=
,
(2)
,
Where, , is the instantons load (power demand) of domestic
bus of parent at a particular time t.
…
H-H Power Share
Yes
Yes
Yes
αpvg > prb,ev ?
Yes
Yes
(αpvb) to battery
Yes
No
(Psev+
Is EV
available?
Yes
b
Qev > Qev
No
Still lsp < ltp ?
Psb+αgpv )>Prb,ev
Fig. 2. Flowchart of the proposed system.
min
?
No
Yes
Qevb < Qevmax ?
Yes
No
ltc > lsc ?
Draw power (Pchev)
from grid
Yes
No
Provide power (Psp→c)
to child
Provide (Psev) to ac bus
No
EV is available?
Draw power (Pch )
from grid
Still lsp < ltp ?
Check load conditions to
continue process
No
b
Yes
No
No
Qbmax > Qbt ?
No
Qbt > Qbmin ?
Provide (Psb) to ac
bus
Provide power
No
No
αpvg + αpvb >0
No
ltp > lsp ?
Yes
Take power
from grid
No
Yes
Y
Provide power (Psp→c)
to child
Still lsp > ltp ?
Yes
Yes
Continue
charging
Still lsp > ltp ?
No
,
=
(3)
−
Likewise, the required power from battery, PV, and EV during
peak-load hours to shave the peak is written as:
,
=
,
The available power
to charge, i.e.
,
is shared between battery and EV
(5)
+
Where,
and
are the charging power for battery and EV
from the grid respectively. Assume the charge of a battery at a
particular time t ( ∈ ) is
(in percentage) and the
maximum charging limit is
(in percentage). The
maximum required power ( ) to charge the battery is
−
100
=
(6)
∗
Where,
is the capacity of the battery. Likewise, the
maximum available power to charge the EV is:
−
100
=
(7)
∗
Where,
is the maximum charging limit,
is the
instantaneous charge of the EV at a particular time t ( ∈ ),
is the capacity of the EV.
The PV power is provided to the AC bus through converter 1,
and a portion is bypassed to the battery through converter 2.
The power provided by PV to the AC bus is expressed as:
=
∙
(8)
<
Where, N is the number of PV module,
is the power
is the efficiency of the
supplied by a single PV module,
converter 1. The power supplied by the PV module to the
battery is
=
∗
=
∗
(
∗
)
−
100
−
100
∗
∗
,
,
,
∈
∈
,
=
,
,
,
→
= (
,
∗
∗
(14)
A schematic of the battery, EV constraints and power sharing
among neighbors is illustrated in Figure 3. It shows the
maximum charging and minimum discharging limit of the
battery storage and EV. The available battery capacity based
on these limits, and the current SOC are calculated for both EV
and battery storage. This capacity is used to minimize the peak
load of the parent. Any excess energy after minimizing the
parent’s peak load is transferred to the child.
ls p
ltp
ls p
Off-peak
ltp
load
Low-limit for
discharging
Chargingg
Qev min
Upper-limit for
charging
Chargingg
Psev
Qev max
Qbmin
P sb
Qbmax
Prb,ev
ltp
ls p
Parent
(11)
(12)
)−
+
Where,
and
are efficiencies of the converters 5 and 6
respectively. A flowchart of the proposed algorithm is
illustrated in Figure 2. Initially, it checks the load condition
(i.e. peak or off-peak load) of the parent. If the parent is in the
peak-load period, the algorithm monitors the amount of power
needed to minimize peak load. Firstly, it utilizes power from
PV, and if the PV power is not enough to supply the peak
demand, it checks available battery capacity based on its SOC
constraints. If the battery storage and PV are not capable of
mitigating the peak demand, the algorithm checks the
availability of EV and its SOC constraints to provide peak load
support. If any power is available (from PV, EV, battery) after
minimizing the peak demand of the parent, it is transferred to
the child based on its demand. On the other hand, if the parent
is in off-peak load condition, the algorithm checks the
available power to charge battery storage and EV by keeping
the load demand in a certain range. The power required to
charge the battery storage and EV is also calculated based on
current SOC and the maximum charging limits. The charging
process continues until the load condition changes. A detailed
algorithm is described in the flowchart in Figure 2.
(10)
Where and are the efficiencies of the converters 3 and 4
respectively. Assume the function of the domestic load curve
of the child is given as:
(13)
=( − )
After meeting the parents demand, the maximum available
power that a child can get from a parent is:
(9)
−
If the minimum discharging limit for battery and EV is
and
, respectively, the maximum available power to shave
the peak by EV and battery is given by:
=
→
(4)
−
=
Where, , is the instantaneous load (power demand) of the
domestic bus of the child at a particular time t ( ∈ ). If the
setpoint to define the peak load and base load period of the
child is , the required power by the child from the parent to
shave the peak is written as:
Peak load
The set point to define the peak and off-peak load is assumed
as , the peak load occurs when > , and off-peak load
occurs when < . So, during base-load hours the available
power to charge the battery and EV is given as:
psp→c
lt c
prc→p
ls c
Child
Fig. 3. Charge-discharge and power sharing schematic of the proposed
algorithm.
Nissan Leaf EV). From this 24 kWh battery capacity of EV,
the V2G-allowed SOC limit is only 15%, i.e. the EV will
discharge its battery only if the SOC goes beyond 85%. Any
flexibility on this limit may provide more load-support to the
parent and the child, however, it may compromise any long and
emergency trip plans of the EV.
IV. RESULTS
The proposed control algorithm has been tested in a real
Australian power distribution networks. The network location
is in Nelson Bay, NSW, Australia. The weather of the area is
incorporated into the system in real-time to get the real PV
power generation and the dynamics of the weather-dependent
loads. The proposed algorithm is implemented in the parent
location to reduce its peak load demand, and the excess energy
is shared with the child. Figure 4 (a) shows the load conditions
of the parent with and without the proposed control algorithm.
It is assumed that the parent consists of energy resources and
the child does not have any energy resources and completely
depends on the grid for its power demand. In this simulation,
the parent consists of a 5 kWh battery storage, a 2 kW PV, and
an EV (24 kWh capacity of EV, the similar capacity of the
The proposed model is simulated for 16 consecutive days to
test its effectiveness in a real scenario, and it is found that the
proposed algorithm can reduce the domestic peak demand of
the parent significantly. The excess energy after reducing the
parent’s peak demand is shared with the child based on its
power demand. Figure 4 (b) shows the load conditions of the
child with and without the proposed control algorithm. From
the findings, it is clear that the proposed method can provide
load-support to the child after meeting parent’s need.
…
(a)
(b)
Fig. 4. Grid peak load reduction and power sharing process, (a). Load conditions of the parent with and without the proposed control algorithm, (b). Load conditions of
the child with and with the power sharing from the parent.
…
V. CONCLUSION
The purpose of the current study was to investigate the
domestic peak load reduction using existing energy resources
and share any excess energy with the neighbor through peerto-peer (P2P) energy transfer process. The results of this
investigation show that the proposed algorithm significantly
reduces the domestic peak load demand for both parent and the
child. It utilizes the local energy sources such as rooftop PVs,
electric vehicles and battery storages to minimize the grid
dependency during high electricity cost periods. The results
reported here shed new light on the domestic peak power
demand reduction process through P2P energy transfer. A
natural progression of this research is to investigate this
method for multiple houses and optimize the path loss between
houses. More study on the control and analysis of converter 6
in multiple houses and complex energy flow conditions would
help to establish a greater degree of accuracy and flexibility on
this matter. Further research could usefully explore how to
optimize peak load and energy resources, and determine
energy billing in a bidirectional energy flow condition.
REFERENCES
[1]
R. Zafar, A. Mahmood, S. Razzaq, W. Ali, U. Naeem, and K.
Shehzad, “Prosumer based energy management and sharing in
smart grid,” Renew. Sustain. Energy Rev., vol. 82, pp. 1675–1684,
2018.
[2]
C. Chen, J. Wang, Y. Heo, and S. Kishore, “MPC-based appliance
scheduling for residential building energy management
controller,” IEEE Trans. Smart Grid, vol. 4, no. 3, pp. 1401–1410,
2013.
[3]
M. Muratori and G. Rizzoni, “Residential demand response:
Dynamic energy management and time-varying electricity
pricing,” IEEE Trans. Power Syst., vol. 31, no. 2, pp. 1108–1117,
2016.
[4]
P. Mesarić and S. Krajcar, “Home demand side management
integrated with electric vehicles and renewable energy sources,”
Energy Build., vol. 108, pp. 1–9, 2015.
[5]
Z. Wang and S. Wang, “Grid power peak shaving and valley
filling using vehicle-to-grid systems,” IEEE Trans. power Deliv.,
vol. 28, no. 3, pp. 1822–1829, 2013.
[6]
M. A. Ortega-Vazquez, F. Bouffard, and V. Silva, “Electric
vehicle aggregator/system operator coordination for charging
scheduling and services procurement,” IEEE Trans. Power Syst.,
vol. 28, no. 2, pp. 1806–1815, 2013.
[7]
K. Mahmud, S. Morsalin, Y. R. Kafle, and G. E. Town, “Improved
peak shaving in grid-connected domestic power systems
combining photovoltaic generation, battery storage, and V2Gcapable electric vehicle,” in 2016 IEEE International Conference
on Power System Technology (POWERCON), 2016, pp. 1–4.
[8]
K. Mahmud, S. Morsalin, M. J. Hossain, and G. E. Town,
“Domestic peak-load management including vehicle-to-grid and
battery storage unit using an artificial neural network,” in 2017
IEEE International Conference on Industrial Technology (ICIT),
2017, pp. 586–591.
[9]
R. Yan, S. Roediger, and T. K. Saha, “Impact of photovoltaic
power fluctuations by moving clouds on network voltage: A case
study of an urban network,” in Power Engineering Conference
(AUPEC), 2011 21st Australasian Universities, 2011, pp. 1–6.
[10] A. Y. Saber and G. K. Venayagamoorthy, “Resource scheduling
under uncertainty in a smart grid with renewables and plug-in
vehicles,” IEEE Syst. J., vol. 6, no. 1, pp. 103–109, 2012.
[11] G. Cau, D. Cocco, M. Petrollese, S. Knudsen Kær, and C. Milan,
“Energy management strategy based on short-term generation
scheduling for a renewable microgrid using a hydrogen storage
system,” Energy Convers. Manag., vol. 87, pp. 820–831, 2014.
[12] M. S. H. Nizami and J. Hossain, “Optimal scheduling of electrical
appliances and DER units for home energy management system,”
in Universities Power Engineering Conference (AUPEC), 2017
Australasian, 2017, pp. 1–6.
[13] M. S. H. Nizami, A. N. M. M. Haque, P. H. Nguyen, and F. W.
Bliek, “HEMS as network support tool: Facilitating network
operator in congestion management and overvoltage mitigation,”
in 2016 IEEE 16th International Conference on Environment and
Electrical Engineering (EEEIC), 2016, pp. 1–6.
[14] F. Sehar, M. Pipattanasomporn, and S. Rahman, “An energy
management model to study energy and peak power savings from
PV and storage in demand responsive buildings,” Appl. Energy,
vol. 173, pp. 406–417, 2016.
[15] M. N. Akter, M. A. Mahmud, and A. M. T. Oo, “A Hierarchical
Transactive Energy Management System for Energy Sharing in
Residential Microgrids,” Energies, vol. 10, no. 12, p. 2098, 2017.
[16] M. N. Akter, M. A. Mahmud, and A. M. T. Oo, “A hierarchical
transactive energy management system for microgrids,” in Power
and Energy Society General Meeting (PESGM), 2016, 2016, pp.
1–5.
[17] B. CELIK, R. Roche, D. Bouquain, and A. Miraoui,
“Decentralized neighborhood energy management with
coordinated smart home energy sharing,” IEEE Trans. Smart
Grid, 2017.
[18] X. Xu, X. Jin, H. Jia, X. Yu, and K. Li, “Hierarchical management
for integrated community energy systems,” Appl. Energy, vol.
160, pp. 231–243, 2015.
[19] K. Orehounig, R. Evins, and V. Dorer, “Integration of
decentralized energy systems in neighbourhoods using the energy
hub approach,” Appl. Energy, vol. 154, pp. 277–289, 2015.
[20] C.-S. Karavas, G. Kyriakarakos, K. G. Arvanitis, and G.
Papadakis, “A multi-agent decentralized energy management
system based on distributed intelligence for the design and control
of autonomous polygeneration microgrids,” Energy Convers.
Manag., vol. 103, pp. 166–179, 2015.