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Cooperation of electric vehicle and energy storage in reactive power compensation: An optimal home energy management system considering PV presence
Accepted Manuscript Title: Cooperation of Electric Vehicle and Energy Storage in Reactive Power Compensation: An Optimal Home Energy Management System Considering PV Presence Author: Sajjad Golshannavaz PII: DOI: Reference:
S2210-6707(17)31609-8 https://doi.org/10.1016/j.scs.2018.02.018 SCS 985
To appear in: Received date: Revised date: Accepted date:
25-11-2017 1-2-2018 14-2-2018
Please cite this article as: & Golshannavaz, Sajjad., Cooperation of Electric Vehicle and Energy Storage in Reactive Power Compensation: An Optimal Home Energy Management System Considering PV Presence.Sustainable Cities and Society https://doi.org/10.1016/j.scs.2018.02.018 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Cooperation of Electric Vehicle and Energy Storage in Reactive Power Compensation: An Optimal Home Energy Management System
Sajjad Golshannavaza,* a
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Considering PV Presence
Electrical Engineering Department, Urmia University, Urmia, Iran
*
Corresponding Author: Sajjad Golshannavaz, Electrical Engineering Department, Urmia University, Urmia,
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Highlights
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Iran. Tel: +984432775660. (Email: [email protected]).
An optimal two-stage HEM strategy is proposed for smart home load management;
Efficient linearization techniques are deployed to convert the proposed HEM to a MILP
M
A
format;
Economic improvements are attained in stage 1 and is kept constant in stage 2;
EV and ESS participate in reactive power compensation of home appliances;
PT
ED
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Power factor correction is conducted in home-to-grid integration point.
Abstract– This paper proposes a home energy management (HEM) strategy to not only reduce the
customer’s billing cost but also to compensate the reactive power at the point of grid integration. The
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developed HEM enables the home owner to manage different components and appliances including electric vehicle (EV), energy storage system (ESS), and shiftable loads (SLs). Optimal scheduling of consumption times of SLs and charging/discharging cycles of EV and ESS ends in sensible reduction in daily operation cost. Then, satisfying the obtained minimum operation cost within the constraints, the remaining capacity of EV and ESS inverters is dedicated to reactive power compensation of the home
1
appliances. Accordingly, the power factor (PF) of home-to-grid connection is sensibly improved. The proposed HEM is modeled as a mixed-integer linear programming (MILP) and is solved with general algebraic modeling system (GAMS). The obtained results approve both the economic and technical
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successes.
Keywords: Home energy management (HEM); cost reduction; reactive power compensation; power factor (PF) improvement. NOMENCLATURE Indices, Sets, and Symbols
t, T
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Index and set of time intervals. Indices of maximum and minimum limits.
N
Indices of appliances.
i
+,-
A
Charging and discharging states.
max, min
Power flow efficiency of PV inverter.
Solar irradiance (kW/m2).
ED
PV panel surface area (m2).
M
Parameters
PV APV
I PV
PT
Price of energy bought from the grid and the price of energy sold to the grid.
sold
t , t
P FL
Shiftable load active power (kW).
P SL
CC E
Fixed load active power (kW).
Maximum exchangeable apparent power with the grid (kVA).
S grid
Up-time of shiftable appliance.
UT
Charging and discharging rates of converter.
CR, DR CE , DE
Big positive number.
M
A
Charging and discharging efficiencies of converter.
Variables and Functions
P PV
PV active power (kW).
2
EV active power (kW).
P EV
ESS active power (kW). Binary variable for on/off states of shiftable loads.
P ESS x
Active and reactive powers imported from grid, respectively.
P
Startup and shutdown binary variables.
su , sd
Auxiliary binary variable in linearization.
a
, Q grid
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Capacitive reactive power injected by EV or ESS inverter (kVAr).
grid
Q Cap
SOE
State of energy.
X , X
Auxiliary positive variables deployed in linearization.
n
Binary variable denoting trapezium number.
pn , qn
N
U
Active and reactive powers corresponding to trapezium n.
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1. Introduction
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Smart home concept refers to the set of hardware and software applications which are embedded to attain higher flexibility in daily consumption of home appliances [1]. Home energy management
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(HEM) system is the main core of these elements evolved for an optimal operation of home
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appliances. Specifically speaking, the smart grid technology and its accompanied two-way communication facilities makes it possible to get access to time varying electricity price signals
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[2]. Accordingly, the consumption pattern of appliances could be controlled based on the electricity rates at off-peak or peak intervals. In this view, the home appliances are divided into three main categories including non-shiftable/fixed loads (FLs), controllable loads, and shiftable
A
loads (SLs). FLs should be continuously and completely supplied when plugged into the circuit. However, the HEM system determines the on/off status of SLs and the best operating intervals of these appliances such that a minimum operation cost is guaranteed [3]. The third type including the controllable loads refers to the loads such as thermostatically-controlled ones. In this type, the
3
distribution system operator could sign a bilateral contract with home owners to control these loads at the emergency conditions and peak power interval management. Besides, integration of energy generation and storage facilities in the distribution and residential networks could enhance the
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economic, technical, and reliability metrics. At the utmost ending point of the networks say in home premises, it is now a common scene to come into view with a small-scale photovoltaic (PV) and energy storage system (ESS) [4]-[5]. Meanwhile, the underlying importance of reducing the global warming and green-house gas emission has brought up prompt progress in clean transportation systems including electric vehicle (EV) [6]. The integration of EVs in distribution
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networks could stimulate new challenges ahead of a successful operation scheme. A well-designed
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HEM should tolerate the presence of EV and make benefit from its positive side-effects in load
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commitment process.
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So far, considerable attempts have been made in devising effective HEM strategies considering different objectives, mainly the customer’s operation cost minimization [7]. In [8]-[10], efficient
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approaches are developed for home load commitment considering the demand responding to
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varying prices. The main purpose is to reduce the customer’s billing cost. However, the presence of EV, ESS, and PV is ignored. The application of ESS to reduce the total operation cost is
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proposed in [11]. In this study, the charging/discharging cycles of ESS is determined at off-peak and peak time intervals to attain the minimum operation cost. Noticeably, the presence of EV and its functionality for peak power shaving have not been tailored. Besides, the renewable energy
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resources are not taken into account. In [12], the charging/discharging process of EV is tailored in home load commitment. Some other studies have investigated the unavailability times of EV and its improvement by ESS unit. To highlight the worth of cooperation between the EV and ESS, authors in [13] have assessed various scenarios for engaging these devices. The obtained results have demonstrated that complementary operation of EV and ESS can provide better performance 4
for demand shifting and complete utilization of generated renewable energy. In [14], an effective HEM is developed to schedule the home appliances, PV generation, EV, and ESS charging/discharging patterns. Although the established approach is a prosperous one, the full
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utilization of ESS and EV functionalities is not modeled. Similar studies are performed in [15] and [16] with the similar pros and cons.
As can be seen, in all of the reviewed literatures, the main purpose is to reduce the daily operation cost and only the active power scheduling of home appliances is concerned. Accordingly, no attention is paid on reactive power issues. This situation is due to the current trend
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in residential billing issues within which the customers are only charged for active power
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consumption. In future grids, similar to commercial and industrial customers, deployment of
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digital and smart meters makes it possible to record the reactive power consumption of a residential
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consumer. In this way, if the power factor (PF) of home-to-grid integration point goes under a permissible level, then penalty value is applied and consumer pays for reactive power
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consumption. Not only in future, there are some utilities that apply reactive power costs in
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residential consumers billing in Netherlands depending on distribution network operator (DSO) strategy [17]. Moreover, in the traditional power systems, homes only accommodate the passive
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elements and hence, the average power factor is >=0.8. However, in the smart grid concept, the home owners are so willing to install small-scale renewable power generation systems. As well, they are equipped with electric vehicle (EV) and energy storage system (ESS) in power
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management of home. In this case, at some specific intervals, the active power requirement of home appliances is locally supplied through renewables, EV, and ESS. Accordingly, a lower active power is transferred from the upstream network at the point of grid integration. Though, all of the required reactive power is supplied from the external grid. This situation results in a lower power factor record at the home-to-grid integration point which at some intervals, it also reaches to zero. 5
This issue puts a high concern on lower power factor of future smart homes and calls the need for efficient HEM systems. Accordingly, it makes sense to include the reactive power processes of home appliances and track the PF of home-to-grid integration point. To the best of the author’s
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knowledge, this issue has not been tailored in the technical and research studies, so far. In an attempt to include both the reactive power process of home appliances and cost minimization issues, this paper proposes an effective two-stage linear HEM strategy. Although the active and reactive powers are scheduled simultaneously in network-based studies, such an approach does not end in optimum economic operation within the smart home premises. This issue
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is due to a lower cost of reactive power imposed to the home owners. The main purpose behind
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developing a two-stage approach is on keeping the economic savings of the home owner as the
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highest priority. Accordingly, in the first stage, the SLs, ESS, and EV are scheduled such that the
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total operation cost of active power exchanges is minimized. To this end, the scheduling time of SLs and charge/discharge cycles of ESS and EV are determined, optimally. However, the reactive
ED
power compensation is not taken into account at this stage and the improvement of PF at home-
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to-grid integration point is not realized. The obtained minimum cost is satisfied within the constraints in the second stage and the objective is to compensate the reactive power at the home-
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to-grid integration point. To correct the PF at this point, the ESS and EV inverters remaining capacity is dedicated to provide reactive power requirements of the home appliances. Accordingly, full utilization of ESS and EV functionalities are deployed for both the economic and technical
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issues. In other words, not only the minimum operation cost is obtained, but also the PF of home is enhanced notably. Such an achievement cannot be easily pursued in integrated active and reactive power schedule inside a home as the reactive power processes would sensibly impact on active power schedule patterns. In contrast, the proposed two-stage approach ends in acceptable results in terms of economic and technical enhancements. The initial mathematical model of the 6
proposed HEM is within a non-linear format which obstructs its deployment in real-world applications. In order to avert this issue, effective linearization techniques are deployed to make the proposed model a linear one. Accordingly, the proposed HEM is represented within a mixed
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integer linear programming (MILP) format and solved based on general algebraic modeling system (GAMS). To make it brief, the main contributions of the proposed HEM could be listed as follows:
An optimal two-stage HEM strategy is proposed for smart home load management;
Efficient linearization techniques are deployed to convert the proposed HEM to a MILP format;
Minimum operation cost is attained in stage 1 and is kept constant in stage 2;
EV and ESS participate in reactive power compensation of home appliances;
PF is enhanced sensibly in home-to-grid integration point in stage 2.
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The remainder of this manuscript is organized as follows: Section II addresses mathematical
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modeling of the proposed HEM. Also, the linearization tricks are applied here to avert the nonlinear nature of the proposed model. Numerical studies are performed in Section III to assess the
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prosperous impacts of the proposed strategy. Higher emphasis is put on cost minimization and PF correction of home-to-grid integration point. Eventually, the concluding remarks are provided in
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section IV.
2. Proposed HEM Strategy: Mathematical Modeling and Linearization Tricks
General illustration of a smart home and its software and hardware components is illustrated in
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Fig.1. According to this figure, the home contains FLs, SLs, rooftop PV, ESS, and EV. The power consumed by FLs could not be scheduled; however, SLs are integrated in optimization process of HEM to be dispatched within the predetermined intervals by the home owner. The EV is scheduled such that it could be involved in both active and reactive power exchanges. The charging converter
7
of EV is a bidirectional converter which can inject or absorb reactive power to the common coupling point. A similar functionality is attained by ESS. Moreover, it does not encounter with the unavailability issues seen in EV departure. All of these devices are linked to HEM to announce the
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essential information and then get the optimal output commands. The initial state of energy (SOE) of ESS and EV, arrival and departure time of EV, sun irradiance of rooftop PV, operation requirement of SLs, and electricity price signals are denoted as the required input signals.
2.1.Objective Functions The proposed model of HEM consists of two stages. In the first stage, the daily operation cost of
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home is minimized. As mentioned earlier, the obtained minimized cost in this stage is assigned within
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the constraints in the second stage and the objective is to maximize the PF of home-to-grid integration
Daily Operation Cost of Home
M
A
point and compensate the reactive power.
In the first stage, the proposed HEM guarantees the minimum daily operation cost of home
ED
according to (1). 144
[ ( P
grid
) ( t
PT
min F
cost
t
t
sold
t ) max 0, Pt
grid
]. t
t 1
(1)
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subject to (3)-(37)
In (1), the first term declares the cost of purchased power from the grid. The developed model
also includes energy selling back to the grid, as denoted by the second term. Energy buy and sell
A
prices namely t and t
sold
are supposed not to be equal. Specifically speaking, the selling price
is assumed to be a bit higher than the energy buying price to encourage the home owner to cooperate in power supply process at peak intervals. Note that the “max” function in (1) is a nonlinear term and is linearized in an exact manner in the following.
8
PF Correction
When the minimum operation cost of smart home is attained in the first stage, the second stage is triggered to maximize the PF at the home-to-grid integration point. As clarified, in this process, the minimum operation cost obtained in the first stage is considered as a constraint and through an
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optimal rescheduling of active elements including SLs, ESS, and EV, the proposed HEM maximizes the PF. To this end, the consumed reactive power of home is minimized compared to the absolute value of active power, demonstrated in (2). Note that the absolute function represents a non-linear term and is properly linearized in the following.
max F
P
grid
t
grid
Qt
U
144
PF
(1)-(25) subject to cost cost F F
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2.2.Components Modeling and Constraints
(2)
A
N
t 1
Active and Reactive Power Balance
ED
Operation scheduling for all devices should be performed such that the active and reactive
PT
power balance should be maintained at each time interval. (3) and (4) are considered.
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Pt grid Pt PV Pt EV Pt ESS Pt EV Pt ESS Pt FL xt ,i PtSL ,i ,
(3)
t
i
Qtgrid QtEV Cap QtESS Cap QtFL xt ,iQtSL ,i ,
t
(4)
i
A
where xt ,i denotes the on/off state of i-th SL at time interval t. Note that 1 represents the “on”
state of each SL and 0 denotes its “off” state. As can be seen in (4), the ESS and EV are scheduled in capacitive mode to compensate the reactive power requirements of home appliances. Besides, the home-to-grid reactive power injection is not allowed in this study. Because, the reactive power
9
injection from the home toward the grid interferes with the Volt/VAr control processes of the network operator and introduces new issues to handle. Hence, this case is not considered; however, the interested readers could go in through this idea as a possible research in future smart home
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interactions. The apparent power of home drawn from or injected to the grid can be restricted by the grid operator in order to avoid the feeder congestion or operation bottlenecks. To do so, (5) is considered.
P Q S
2
grid t
2
grid t
2
,
t
(5)
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grid
t
Modeling of SLs
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Scheduling of operation time intervals of SLs is performed by the proposed HEM to attain an
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optimum operation point for the whole system. Meanwhile, operation constraints of SLs are
x
i ,t
UTi ,
i
t UTi 1
xi , k UTi sui ,t ,
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k t
sui ,t - sdi ,t xi ,t - xi ,t 1,
t 144 UTi 1, i.
t , i.
(7)
(8)
t , i.
(9)
A
sui ,t sdi ,t 1,
(6)
PT
t si
ED
fi
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presented by (6) to (9).
As an important technical constraint, the operation time of i-th SL should be equal to the up time
(UT) of that device which should be in the allowable time interval declared by the start and finish intervals, represented by si and fi . These requirements are modeled by (6). The operation time
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intervals of SLs should be consecutive which is modeled by (7). Moreover, startup/shutdown variables and on/off states are mathematically related to each other by (8). Each SL cannot be on and off simultaneously at each time interval. Accordingly, (9) is included. Modeling of EV
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As it is well-recognized, vehicle-to-home (V2H) concept is developed to make benefit of EV opportunities for optimizing the home interactions. The conceptual illustration of the EV power exchange with the home is shown in Fig. 2. The EV can store the energy in its battery and discharge it back to the grid at the required time intervals. Besides, its converter can participate in reactive
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power exchanges in inductive or capacitive modes. The reactive power transfer only involves the
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converter of EV and does not pose any burden on its battery. However, the reactive power
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provision of EV converter restricts the opportunity of active power flow through it. The operation
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of EV converter on active and reactive power provision is determined by the HEM to optimize the operation of smart home.
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EVs operation scheduling depends on its availability in home. In unavailability time intervals no task should be accounted for EVs. The unavailability of EV is specified by the departure and
PT
arrival times represented by tD and t A , respectively. At each time interval, EV could be only in EV
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one of the charging or discharging modes represented by xt
EV
and xt
, respectively. Thus, the
interactions of EV are formulated by (10) to (19). EV
A
xt
xtEV 1,
t
PtEV CREV .xtEV ,
(10)
t
PtEV DE EV DREV .xtEV ,
PtEV PtEV PtEV ,
(11)
t
(12)
t
(13)
11
P Q EV
t
2
EV Cap t
S 2
EV max
2
,
t
(14)
EV SOEtEV SOEinitial , t 1 1
SOE EV ,min SOEtEV SOE EV ,max ,
1 DE EV
.Pt EV .t,
t tD , t tA
(16)
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SOEtEV SOEtEV CE EV .Pt EV .t 1
(15)
t
(17)
SOEtEV SOEREV , t tD
(18)
SOEtEV SOEtEV SOEREV , t tA 1
(19)
D
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In (10), the EV cannot be simultaneously within the charge and discharge modes. The charging
N
and discharging power of EV battery are restricted by the maximum charge and discharge rates in
A
(11) and (12), respectively. The net active power of EV is calculated by (13). As mentioned earlier,
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the EV converter is additionally capable to exchange reactive power with AC bus. Therefore, the apparent power should not exceed the converter rating according to (14). The SOE of EV battery
ED
at initial time interval of scheduling is equal to the residual energy from the previous operation day modeled by (15). The SOE of EV battery at intraday time intervals is calculated by (16). The
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SOE of battery should be restricted in permissible range denoted by (17). To provide the required
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energy for a specific travel distance, the SOE of battery at departure time should be higher than the required SOE for that travel, satisfied by (18). Eventually, the SOE at the arrival time is calculated by (19).
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Modeling of ESS
Application of ESS in smart home fulfills the energy storage requirements along with EV. In
the proposed HEM strategy, ESS is modeled within (20) to (27). ESS
xt
xtESS 1,
t
(20)
12
PtESS CRESS .xtESS ,
t
(21)
PtESS DE ESS DRESS .xtESS ,
PtESS PtESS PtESS , ESS
2
ESS Cap t
t
(22)
t
(23)
S 2
2
ESS max
,
t
SC RI PT
P Q
t
(24)
ESS SOEtESS SOEinitial , t 1 1
(25)
SOEtESS SOEtESS CE ESS .Pt ESS .t 1 SOE ESS ,min SOEtESS SOE ESS ,max ,
1 DE ESS
.Pt ESS .t,
t
t
(26)
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(27)
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Similar to EV modeling procedure, converter of ESS is also capable of injecting or absorbing
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reactive power. The important point to be mentioned is that the operation mode of EV and ESS in
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reactive power provision should be the same. That means the converters of EV and ESS should be in the same mode of either injection or absorption of reactive power. In order to avoid opposite
Q
EV Cap
atQt
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EV Cap t
Q
EV Cap t
Q
A
Q
ESS Cap t
ESS Cap t
EV Cap
(28)
(29)
M
(30)
Q
ESS Cap
atQt
M
1 atQt
QtESS Cap QtESS Cap
Q
EV Cap t
PT
QtEV Cap QtEV Cap
ED
operation mode, (28) to (34) are included in EV and ESS modeling.
(31)
M
ESS Cap
1 atQt
ESS Cap t
(32)
M
(33)
13
EV Cap
atQt
ESS Cap
atQt
(34)
Note that (28)-(30) and (31)-(33) specify the sign of QtEV Cap and QtESS Cap , respectively. If the EV Cap
ESS Cap
atQt
or
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reactive power of EV or ESS has a positive sign, the corresponding binary variable atQt
should be 1 and vice versa. Accordingly, (34) denotes that the sign of reactive power for
EV and ESS should be the same. This issue is in regard of the sign of reactive powers of EV and ESS. This constraint is included to avert the case in which the ESS is in capacitive mode and EV is in inductive mode or vice versa. In other words, one of them generates reactive power and the other
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one consumes the reactive power which is a bad and wrongly tuned operation condition. The EV
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and ESS should not interact with each other and only should provide the reactive power requirements
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to assure a technically-satisfied operation.
A
of the appliances. Accordingly, the sign of EV and ESS reactive powers is assigned to be the same
Modeling of PV
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The maximum active power which can be produced by PV unit depends on solar irradiance and
PT
is calculated by (35). The PV active power generation at each time interval should be less than its maximum value stated in (36). Also, the produced power should be less than PV converter capacity
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as denoted by (37). PV
Pt
PV APV I PV
(35)
PV
(36)
PtPV S PV
(37)
A
Pt PV Pt
2.3.Linearization Tricks The developed mathematical model of HEM in (1)-(37) demonstrates nonlinear terms ending to a mixed-integer non-linear programming (MINLP) format. To avert the non-linear nature of the
14
proposed model, (1), (2), (5), (14) and (24) should be linearized. To this end, the following linearization techniques are deployed.
Maximum Function: max(A, B)
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The maximum function in (1) is a discrete nonlinear function. This function can be linearized as follow: A Mamin B A
(38)
min B MaB A
(39)
min amin aB 1 A
U
(40)
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L max(A,B) A
M
A
L max(A,B) B
L min max(A,B) B MaB
ED
L max(A,B) A Mamin A
(41) (42) (43) (44)
amin A
min
and aB
are the binary variables specifying the minimum of variables A and B by taking
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Here,
PT
L In (38)-(44), the maximum of two variables A and B, max (A,B) , is calculated in a linear format.
the value of 1, respectively. Then, based on (41)-(44), the maximum variable is chosen.
Absolute value
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The absolute function denotes a discrete nonlinear function which can be linearized as follow.
Pt grid =ΔPt grid -ΔPt grid
(45)
Pt grid =ΔPt grid ΔPt grid
(46)
0 ΔPt grid atP M
(47)
grid
15
0 ΔPt grid 1 atP
grid
M
(48)
P-Q circle
The apparent power limitation of home-to-grid integration point, ESS, and EV converter transfer
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capacity declared in (5), (14), and (24) demonstrate a nonlinear form. To linearize these equations, a trapezoidal method is applied [18]. Here, (49)-(54) represent the linearization process in which subscripts l and r denote the left and right upper corner values of n-th trapezium in PQ coordinate system, respectively. Also n is a binary variable that specifies which trapezium is selected.
Trap
1, 2,..., N Trap
qn ,
Trap
1, 2,..., N Trap
n
Trap
Trap
M n qn M n
n 1
Trap
n,r
(50) (51) (52)
n,l
(53) (54)
PT
n
M
Pn,r Pn,l
Q Q
P Pn,l qn Qn,l Pn,r Pn,l P Pn,l
ED
Qn,r Qn,l
A
n Pn,l pn n Pn,r Qn,l
(49)
U
pn ,
n
Q
N
P
3. Case Studies and Numerical Results
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3.1. Typical Smart Home Specifications The proposed HEM is simulated on a typical smart home which encompasses FLs, SLs, EV,
ESS, and PV units. The consumption data of the FLs is listed in TABLE I whereas three SLs and
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their operating intervals are listed in TABLE II. Note that the PF values of all appliances are extracted from [19]-[20]. It is of significance to mention that the scheduling time interval is assumed to be 10 minutes based on which a typical day is represented with 24 6 144 intervals.
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The smart home hosts an EV which its presence is depended on the home owner’s driving behavior. This EV is unavailable between time intervals 45 to 104. In the remaining time intervals, it is parked at the garage and can be participated in home load commitment. In the conducted study,
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the simulated EV is Chevy Volt which its technical data is listed in TABLE III. Moreover, the technical data of ESS are listed in TABLE IV.
A 3 kW rooftop PV is considered to be installed at home. The daily solar irradiance and PV generation is demonstrated in Fig. 3. Besides, the electricity price provided form the external grid is illustrated in Fig. 4 [20].
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3.2. Case Studies
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The developed model for HEM is investigated in two case studies. In Case1, only the operation
A
cost of home is optimized. In other words, this case corresponds to the first stage of the proposed
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HEM where the only objective is to minimize the daily operation cost. In Case2, the HEM is assessed based on the assumptions of the second stage in which the PF of the home-to-grid integration point
ED
is also maximized. In this process, the obtained minimum cost at the first stage is kept constant and
PT
the full utilization of EV and ESS functionalities is performed. The obtained results for both case studies are discussed, comparatively. The proposed MILP HEM strategy is modeled in GAMS®
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and solved based on CPLEX® 12.5 with zero value of relative gap. It is worth mentioning that the resolution time of 10 minute is applied in simulation studies. The charging power profile of EV and ESS are depicted in Figs. 5 and 6. As can be seen, the
A
charging task of EV and ESS is performed at the initial time intervals with lower electricity prices. However, there are not similar discharge patterns for EV and ESS; since the EV is unavailable in some specific time intervals. From the results, the most suitable time intervals for discharging of energy is coincident with peak power intervals with higher electricity price. Charging at off-peak intervals with lower electricity rates and discharging the stored power at peak power intervals averts 17
purchasing the electricity from the external grid with higher rates. Thus, the operation cost reduces. EV and ESS charge/discharge cycles are depicted in Figs. 7 and 8. As can be seen, some differences are noticed comparing the Case1 and Case2. Note that the reactive power provision of EV and ESS
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in Case1 is zero. However, in Case2, EV and ESS are involved in reactive power provision and as can be seen, the charge/discharge cycles of these components differ with those of Case1. Specifically speaking, in Case2, HEM utilizes the remaining capacity of ESS and EV interfacing converter to compensate the reactive power of FLs and SLs, locally. The reactive power provision of EV and ESS are shown in Figs. 9 and 10. As can be seen, the reactive power provision of EV and ESS is
U
increased in peak load time intervals to meet the requirements of the FLs and SLs. This trend
N
influences the active and reactive power exchanges of home with the grid. The active power import
A
from the grid is demonstrated in Fig. 11. As can be seen, in midday time intervals, the imported
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power is decreased. However, the differences in Case 1 and Case 2 are not so huge. Besides, at the time intervals with higher electricity rates, the home exports active power to the grid, i.e., it sells
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back the stored power to the grid to attain higher monetary benefits. A similar graph is provided for
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the reactive power exchanges in Fig. 12. The reactive power is recorded at the PCC and relates to the aggregated loads. In Case 1, all of the required reactive power of home appliances is provided
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form the external grid. However, in Case 2, at some intervals, EV and ESS provide a portion of the required reactive power inside the home and the remaining amount is supplied form the external grid. Also, at some other intervals, the reactive power requirement of the home appliances is totally
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provided from the ESS and EV inside the home. In other words, zero reactive power is provided from the external grid. The imported reactive power from the grid to the home is demonstrated to study the PF of the home at the grid integration point. Fig. 13 depicts the PF obtained at this point. As can be seen, participating of ESS and EV in reactive power provision in Case2 reduces the reactive power burden of the network and hence, enhances the PF profile. The PF is sensibly 18
increased toward unity and it meets the minimum permissible PF triggered in financial penalty calculations by most of the utilities. As explained, in the smart grid concept, the home owners are willing to install small-scale renewable power generation systems. As well, they are equipped with
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EV and ESS in power management of the home. In this situation, at some specific intervals, the active power requirement of home appliances is locally supplied inside the home and a lower active power is transferred from the upstream network at the point of grid integration. Though, all of the required reactive power is supplied from the external grid (Case 1). This situation results in a lower power factor at the home-to-grid integration point which at some intervals, it also reaches to zero.
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This issue puts a high concern on lower PF of future smart homes and calls the need for efficient
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HEM systems dealing with reactive power processes, too. In the proposed model and in Case 2 which
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incorporates the reactive power provision of EV and ESS, a portion of the reactive power of home
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appliances is satisfied inside the home and the remaining is provided form the external grid. Hence, the PF is increased at the home-to-grid integration point.
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The overall daily operation cost is recorded in Table V. It can be inferred that at the second stage,
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maximization of the PF does not incur any financial losses and the obtained minimum cost at the first stage remains constant.
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Scheduling of SLs is shown in Fig. 14. As can be seen, these appliances are similarly scheduled at the investigated cases and are turned on at two specific time intervals. The first interval includes PV availability intervals with significant power generation and the second one refers to the intervals
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with lower electricity price. Eventually, the total operation cost of home is displayed in Fig. 15 for both cases. As can be seen, slight changes are noticed in operation cost; however, the total cost is kept at minimum value in Case1 which is 412.9$. In some specific intervals within which the power production of PV increases or the selling tariff grows, the operation cost is negative and the home exports active power to the grid. 19
6. Conclusions A two stage efficient HEM strategy furnished by SLs, PV, EV, and ESS was developed in this paper. In the first stage, the operation cost of home was minimized while the PF was
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improved in the second stage. To this end, an efficient mathematical model was developed and proper linearization techniques were deployed to avert the non-linear nature of the established model. Deployment of EV and ESS storage capacity on active power exchanges resulted in an optimal charge/discharge patterns and decreased the operation cost of home significantly. Besides, the reactive power provision by EV and ESS converters was properly included in
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reactive power compensation of home appliances. More interestingly, this feature did not
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threaten the economic success of the proposed HEM in the first stage and the obtained
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improvements were only afforded as side-effect opportunities. Accordingly, a significant
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improvement was noticed in PF correction of the home-to-grid integration point. By this way, lower burden of reactive power is incurred from the home to the grid and the DSO is encountered
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a more facilitated operation of grid at the upper levels. This point is an interesting outcome in
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smart grid studies dealing with interactions of smart home and distribution system operations. References
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[1] N. G. Paterakis, O. Erdinç, A. G. Bakirtzis and J. P. S. Catalão, “Optimal Household Appliances Scheduling Under Day-Ahead Pricing and Load-Shaping Demand Response Strategies,” IEEE Trans. Ind. Inform., vol. 11, no. 6, pp. 1509-1519, Dec. 2015.
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[2] K. M. Tsui and S. C. Chan, “Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1812-1821, Dec. 2012.
[3] F. L. Meng and X. J. Zeng, “A Profit Maximization Approach to Demand Response Management with Customers Behavior Learning in Smart Grid,” IEEE Trans. Smart Grid, vol. 7, no. 3, pp. 1516-1529, May 2016.
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[4] I. Dincer and Canan Acar “A review on clean energy solutions for better sustainability,” International Journal of Energy Research, vol. 39, no. 5, pp. 585-606, April 2015. [5] I. Dincer and M.A. Rosen “A worldwide perspective on energy, environment and sustainable development,” International Journal of Energy Research, vol. 22, no. 15, pp. 1305-1321, December 1998.
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[6] F. Y. Melhem, O. Grunder, Z. Hammoudan and N. Moubayed, “Optimization and Energy Management in Smart Home Considering Photovoltaic, Wind, and Battery Storage System With Integration of Electric Vehicles,” Canadian Journal of Electrical and Computer Engineering, vol. 40, no. 2, pp. 128-138, Spring 2017.
[7] A. Mahmood, N. Javaid, M. Asghar Khan and A. Razzaq “An overview of load management techniques in smart grid,” International Journal of Energy Research, vol. 39,
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no. 11, pp. 1437-1450, September 2015.
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[8] D. Setlhaolo, X. Xia and J. Zhang, “Optimal Scheduling of Household Appliances for Demand Response,” Electric Power Systems Research, vol. 116, pp. 24-28, November
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2014.
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[9] J. H. Yoon, R. Bladick and A. Novoselac, “Demand Response for Residential Buildings
September 2014.
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based on Dynamic Price of Electricity,” Energy and Buildings, vol. 80, pp. 531-541, [10]J. Katz, F.M. Andersen, and P.E. Morthorst, “Load-shift Incentives for Household Demand Response: Evaluation of Hourly Dynamic Pricing and Rebate Schemes in a Wind-based
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Electricity System,” Energy, vol. 115, pp. 1602-1616, November 2016. [11]X. Li and S. Hong, “User-expected price-based demand response algorithm for a home-to-
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grid system,” Energy, vol. 64, pp. 437–449, Jan. 2014.
[12]L. Jian, H. Xue, G. Xu, X. Zhu, D. Zhao and Z. Y. Shao, "Regulated Charging of Plug-in Hybrid Electric Vehicles for Minimizing Load Variance in Household Smart Microgrid,"
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in IEEE Trans. Ind. Electron, vol. 60, no. 8, pp. 3218-3226, Aug. 2013.
[13]R. Lamedica, S. Teodori, G. Carbone, E. Santini, “An energy management software for smart buildings with V2G and BESS,” Sustainable Cities and Society, vol. 19, pp. 173-183, 2015.
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[14]Q. Wei, D. Liu, G. Shi, Y. Liu, “Multibattery optimal coordination control for home energy management systems via distributed iterative adaptive dynamic programming,” IEEE Trans. Ind. Electron., vol. 62, no. 7, pp. 4203-4214, Jul. 2015. [15]M. Rastegar, M. Fotuhi-Firuzabad, F. Aminifar, “Load commitment in a smart home,” Applied Energy, vol. 96, pp. 45-54, 2012.
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[16]O. Erdinc, N. G. Paterakis, T. D. P. Mendes, A. G. Bakirtzis, J. P. S. Catalao, “Smart household operation considering bi-directional EV and ESS utilization by real-time pricingbased DR,” IEEE Trans. Smart Grid, vol. 6, no. 3, pp. 1281-1291, May 2015.
[17]A EURELECTRIC paper, “Network Tariff Structure for a Smart Energy System,” available online: http://www.eurelectric.org/media/80239/ 20130409_network-tariffspaper_final_to_publish-2013-030-0409-01-e.pdf
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[18]A. Hamidi, S. Golshannavaz and D. Nazarpour, “D-FACTS Cooperation in Renewable
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Integrated Microgrids: A Linear Multi-Objective Approach,” IEEE Trans. Sustainable Energy, 2017, doi: 10.1109/TSTE.2017 .2723163
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[19]A.S. Pabla, Electric Power Distribution, McGraw-Hill Education, pp. 867-870, 2004.
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[20]O. Erdinc, “Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households,” Applied Energy,
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PT
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vol. 126, pp. 142-150, 2014.
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AC Point of common coupling with grid
Information signals from smart grid operator
Shiftable loads
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HEM Fixed loads
Fig.1. Data and power flow in a smart home.
CE EV
1 P EV DE EV
DE EV
P EV
SL / FL P EV
AC Bus
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CE EV P EV
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A
N
Q EV Cap
EV Charger DC/AC Converter
ED 3 2.5 2 1.5 1 0.5 0
1
12 23 34 45 56 67 78 89 100 111 122 133 144 Time interval
Fig. 3. Daily power generation by PV.
A
CC E
PT
Active power (kW)
Fig.2. Conceptual illustration of V2H.
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External Grid
Electricity price ($/kWh)
5 4 3 2 1 0
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1 12 23 34 45 56 67 78 89 100 111 122 133 144 Time interval
4
2
U
0
-4
N
-2
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139
EV power (kW)
Fig. 4. Electricity price from the grid.
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Time interval (10 min)
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ED
4 3 2 1 0 -1 -2 -3 -4
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 Time interval (10 min)
Fig. 6. Charge/discharge cycles of ESS in Case1.
4
2 0 -2 -4
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139
EV power (kW)
A
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PT
ESS power (kW)
Fig. 5. Charge/discharge cycles of EV in Case1.
Time interval (10 min)
24
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4 3 2 1 0 -1 -2 -3 -4
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139
ESS powe r (kW)
Fig. 7. Charge/discharge cycles of EV in Case2.
Time interval (10 min)
U N A
M
3.5 3 2.5 2 1.5 1 0.5 0
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139
Re active power (kVar)
Fig. 8. Charge/discharge cycles of ESS in Case2.
Time interval
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3.5 3 2.5 2 1.5 1 0.5 0
1
12 23 34 45 56 67 78 89 100 111 122 133 144 Time interval
Fig. 10. Reactive power provision of ESS in Case2.
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PT
Re active power (kVar)
Fig. 9. Reactive power provision of EV in Case2.
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Active power (kW)
15
Case1 Case2
10 5 0 -5
-10 12
23 34 45 56 67 78 89 100 111 122 133 144 Time interval
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1
Fig. 11. Active power import/export from/to the grid.
Re active power (kVar)
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Case1 Case2
4 2
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0
-2
12 23 34 45 56 67 78 89 100 111 122 133 144 Time interval
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1
M
A
Fig. 12. Reactive power import from the gird.
Powe r factor
1
ED
0.8 0.6 0.4
Case1 Case2
PT
0.2
0
12
23 34 45 56 67 78 89 100 111 122 133 144 Time interval
Fig. 13. PF record at the home-to-grid integration point.
4
Active power (kW)
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1
3 2 1
0 1
12
23 34 45 56 67 78 Time interval
89 100 111 122 133 144
Fig. 14. Active power consumption of SLs in Case1 and Case2.
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25 Case1 Case2
Cost ($)
15 5 -5
-15 1
12
23
34
45
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-25
56 67 78 89 100 111 122 133 144 Time interval
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CC E
PT
ED
M
A
N
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Fig. 15. Smart home operation cost in Case1 and Case2.
27
TABLE I FLS SPECIFICATIONS
1.20 2.40 0.22 2.00 1.66 0.08 1.14 0.28 0.20 0.20 0.60
0.93 0.95 0.60 1.00 0.65 0.80 0.90 0.95 0.95 0.80 0.75
06:30-07:00 and 19:30-20:00 19:30-20:00 06:30-07:30 and 19:30-20:30 07:00-07:30 and 20:00-20:30 24 hour 06:30-07:30 and 20:00-22:00 00:00-07:00 and 19:00-24:00 22:00-24:00 20:30-22:00 06:00-07:30 and 19:00-24:00 20:30-21:00
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Microwave Oven Ventilation fan Kettle Refrigerator Radio Air conditioner TV Desktop computer Lights Vacuum cleaner
Daily working time
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PF
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Power (kW)
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Appliance
TABLE II
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SLS SPECIFICATION
PT
CC E
Clothes dryer Dishwasher Washing machine
Power (kW)
PF
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Appliance
3.8 1.32 1.4
1.00 0.70 0.57
EV SPECIFICATIONS Value
EV
SOE
EV ,min
A
SOE
1 hour-once 1 hour-once 1 hour-once
TABLE III
Technical parameter EV ,max
Permitted Interval UT & frequency
Chevy Volt 16 kWh 1.6 kWh
EV
8 kWh
EV
SOER
12 kWh
EV max
3.3 kVA
SOEinitial
S
EV ,max
CR DREV ,max
3.3 kW 3.3 kW
28
si
fi
115 45 45
144 114 114
CE EV , DE EV
0.95
TABLE IV ESS SPECIFICATIONS
SOE
ESS ,max
SOE
ESS ,min
Value
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Technical parameter
10 kWh 1 kWh
ESS
SOEinitial
3 kWh
ESS max
S
CR
DR
3 kW
ESS ,max
ESS
, DE
3 kW
ESS
0.95
A
CC E
PT
ED
M
A
N
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CE
3 kVA
ESS ,max
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S2210-6707(17)31609-8 https://doi.org/10.1016/j.scs.2018.02.018 SCS 985
To appear in: Received date: Revised date: Accepted date:
25-11-2017 1-2-2018 14-2-2018
Please cite this article as: & Golshannavaz, Sajjad., Cooperation of Electric Vehicle and Energy Storage in Reactive Power Compensation: An Optimal Home Energy Management System Considering PV Presence.Sustainable Cities and Society https://doi.org/10.1016/j.scs.2018.02.018 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Cooperation of Electric Vehicle and Energy Storage in Reactive Power Compensation: An Optimal Home Energy Management System
Sajjad Golshannavaza,* a
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Considering PV Presence
Electrical Engineering Department, Urmia University, Urmia, Iran
*
Corresponding Author: Sajjad Golshannavaz, Electrical Engineering Department, Urmia University, Urmia,
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Highlights
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Iran. Tel: +984432775660. (Email: [email protected]).
An optimal two-stage HEM strategy is proposed for smart home load management;
Efficient linearization techniques are deployed to convert the proposed HEM to a MILP
M
A
format;
Economic improvements are attained in stage 1 and is kept constant in stage 2;
EV and ESS participate in reactive power compensation of home appliances;
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Power factor correction is conducted in home-to-grid integration point.
Abstract– This paper proposes a home energy management (HEM) strategy to not only reduce the
customer’s billing cost but also to compensate the reactive power at the point of grid integration. The
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developed HEM enables the home owner to manage different components and appliances including electric vehicle (EV), energy storage system (ESS), and shiftable loads (SLs). Optimal scheduling of consumption times of SLs and charging/discharging cycles of EV and ESS ends in sensible reduction in daily operation cost. Then, satisfying the obtained minimum operation cost within the constraints, the remaining capacity of EV and ESS inverters is dedicated to reactive power compensation of the home
1
appliances. Accordingly, the power factor (PF) of home-to-grid connection is sensibly improved. The proposed HEM is modeled as a mixed-integer linear programming (MILP) and is solved with general algebraic modeling system (GAMS). The obtained results approve both the economic and technical
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successes.
Keywords: Home energy management (HEM); cost reduction; reactive power compensation; power factor (PF) improvement. NOMENCLATURE Indices, Sets, and Symbols
t, T
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Index and set of time intervals. Indices of maximum and minimum limits.
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Indices of appliances.
i
+,-
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Charging and discharging states.
max, min
Power flow efficiency of PV inverter.
Solar irradiance (kW/m2).
ED
PV panel surface area (m2).
M
Parameters
PV APV
I PV
PT
Price of energy bought from the grid and the price of energy sold to the grid.
sold
t , t
P FL
Shiftable load active power (kW).
P SL
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Fixed load active power (kW).
Maximum exchangeable apparent power with the grid (kVA).
S grid
Up-time of shiftable appliance.
UT
Charging and discharging rates of converter.
CR, DR CE , DE
Big positive number.
M
A
Charging and discharging efficiencies of converter.
Variables and Functions
P PV
PV active power (kW).
2
EV active power (kW).
P EV
ESS active power (kW). Binary variable for on/off states of shiftable loads.
P ESS x
Active and reactive powers imported from grid, respectively.
P
Startup and shutdown binary variables.
su , sd
Auxiliary binary variable in linearization.
a
, Q grid
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Capacitive reactive power injected by EV or ESS inverter (kVAr).
grid
Q Cap
SOE
State of energy.
X , X
Auxiliary positive variables deployed in linearization.
n
Binary variable denoting trapezium number.
pn , qn
N
U
Active and reactive powers corresponding to trapezium n.
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1. Introduction
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Smart home concept refers to the set of hardware and software applications which are embedded to attain higher flexibility in daily consumption of home appliances [1]. Home energy management
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(HEM) system is the main core of these elements evolved for an optimal operation of home
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appliances. Specifically speaking, the smart grid technology and its accompanied two-way communication facilities makes it possible to get access to time varying electricity price signals
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[2]. Accordingly, the consumption pattern of appliances could be controlled based on the electricity rates at off-peak or peak intervals. In this view, the home appliances are divided into three main categories including non-shiftable/fixed loads (FLs), controllable loads, and shiftable
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loads (SLs). FLs should be continuously and completely supplied when plugged into the circuit. However, the HEM system determines the on/off status of SLs and the best operating intervals of these appliances such that a minimum operation cost is guaranteed [3]. The third type including the controllable loads refers to the loads such as thermostatically-controlled ones. In this type, the
3
distribution system operator could sign a bilateral contract with home owners to control these loads at the emergency conditions and peak power interval management. Besides, integration of energy generation and storage facilities in the distribution and residential networks could enhance the
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economic, technical, and reliability metrics. At the utmost ending point of the networks say in home premises, it is now a common scene to come into view with a small-scale photovoltaic (PV) and energy storage system (ESS) [4]-[5]. Meanwhile, the underlying importance of reducing the global warming and green-house gas emission has brought up prompt progress in clean transportation systems including electric vehicle (EV) [6]. The integration of EVs in distribution
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networks could stimulate new challenges ahead of a successful operation scheme. A well-designed
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HEM should tolerate the presence of EV and make benefit from its positive side-effects in load
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commitment process.
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So far, considerable attempts have been made in devising effective HEM strategies considering different objectives, mainly the customer’s operation cost minimization [7]. In [8]-[10], efficient
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approaches are developed for home load commitment considering the demand responding to
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varying prices. The main purpose is to reduce the customer’s billing cost. However, the presence of EV, ESS, and PV is ignored. The application of ESS to reduce the total operation cost is
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proposed in [11]. In this study, the charging/discharging cycles of ESS is determined at off-peak and peak time intervals to attain the minimum operation cost. Noticeably, the presence of EV and its functionality for peak power shaving have not been tailored. Besides, the renewable energy
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resources are not taken into account. In [12], the charging/discharging process of EV is tailored in home load commitment. Some other studies have investigated the unavailability times of EV and its improvement by ESS unit. To highlight the worth of cooperation between the EV and ESS, authors in [13] have assessed various scenarios for engaging these devices. The obtained results have demonstrated that complementary operation of EV and ESS can provide better performance 4
for demand shifting and complete utilization of generated renewable energy. In [14], an effective HEM is developed to schedule the home appliances, PV generation, EV, and ESS charging/discharging patterns. Although the established approach is a prosperous one, the full
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utilization of ESS and EV functionalities is not modeled. Similar studies are performed in [15] and [16] with the similar pros and cons.
As can be seen, in all of the reviewed literatures, the main purpose is to reduce the daily operation cost and only the active power scheduling of home appliances is concerned. Accordingly, no attention is paid on reactive power issues. This situation is due to the current trend
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in residential billing issues within which the customers are only charged for active power
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consumption. In future grids, similar to commercial and industrial customers, deployment of
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digital and smart meters makes it possible to record the reactive power consumption of a residential
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consumer. In this way, if the power factor (PF) of home-to-grid integration point goes under a permissible level, then penalty value is applied and consumer pays for reactive power
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consumption. Not only in future, there are some utilities that apply reactive power costs in
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residential consumers billing in Netherlands depending on distribution network operator (DSO) strategy [17]. Moreover, in the traditional power systems, homes only accommodate the passive
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elements and hence, the average power factor is >=0.8. However, in the smart grid concept, the home owners are so willing to install small-scale renewable power generation systems. As well, they are equipped with electric vehicle (EV) and energy storage system (ESS) in power
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management of home. In this case, at some specific intervals, the active power requirement of home appliances is locally supplied through renewables, EV, and ESS. Accordingly, a lower active power is transferred from the upstream network at the point of grid integration. Though, all of the required reactive power is supplied from the external grid. This situation results in a lower power factor record at the home-to-grid integration point which at some intervals, it also reaches to zero. 5
This issue puts a high concern on lower power factor of future smart homes and calls the need for efficient HEM systems. Accordingly, it makes sense to include the reactive power processes of home appliances and track the PF of home-to-grid integration point. To the best of the author’s
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knowledge, this issue has not been tailored in the technical and research studies, so far. In an attempt to include both the reactive power process of home appliances and cost minimization issues, this paper proposes an effective two-stage linear HEM strategy. Although the active and reactive powers are scheduled simultaneously in network-based studies, such an approach does not end in optimum economic operation within the smart home premises. This issue
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is due to a lower cost of reactive power imposed to the home owners. The main purpose behind
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developing a two-stage approach is on keeping the economic savings of the home owner as the
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highest priority. Accordingly, in the first stage, the SLs, ESS, and EV are scheduled such that the
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total operation cost of active power exchanges is minimized. To this end, the scheduling time of SLs and charge/discharge cycles of ESS and EV are determined, optimally. However, the reactive
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power compensation is not taken into account at this stage and the improvement of PF at home-
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to-grid integration point is not realized. The obtained minimum cost is satisfied within the constraints in the second stage and the objective is to compensate the reactive power at the home-
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to-grid integration point. To correct the PF at this point, the ESS and EV inverters remaining capacity is dedicated to provide reactive power requirements of the home appliances. Accordingly, full utilization of ESS and EV functionalities are deployed for both the economic and technical
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issues. In other words, not only the minimum operation cost is obtained, but also the PF of home is enhanced notably. Such an achievement cannot be easily pursued in integrated active and reactive power schedule inside a home as the reactive power processes would sensibly impact on active power schedule patterns. In contrast, the proposed two-stage approach ends in acceptable results in terms of economic and technical enhancements. The initial mathematical model of the 6
proposed HEM is within a non-linear format which obstructs its deployment in real-world applications. In order to avert this issue, effective linearization techniques are deployed to make the proposed model a linear one. Accordingly, the proposed HEM is represented within a mixed
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integer linear programming (MILP) format and solved based on general algebraic modeling system (GAMS). To make it brief, the main contributions of the proposed HEM could be listed as follows:
An optimal two-stage HEM strategy is proposed for smart home load management;
Efficient linearization techniques are deployed to convert the proposed HEM to a MILP format;
Minimum operation cost is attained in stage 1 and is kept constant in stage 2;
EV and ESS participate in reactive power compensation of home appliances;
PF is enhanced sensibly in home-to-grid integration point in stage 2.
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The remainder of this manuscript is organized as follows: Section II addresses mathematical
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modeling of the proposed HEM. Also, the linearization tricks are applied here to avert the nonlinear nature of the proposed model. Numerical studies are performed in Section III to assess the
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prosperous impacts of the proposed strategy. Higher emphasis is put on cost minimization and PF correction of home-to-grid integration point. Eventually, the concluding remarks are provided in
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section IV.
2. Proposed HEM Strategy: Mathematical Modeling and Linearization Tricks
General illustration of a smart home and its software and hardware components is illustrated in
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Fig.1. According to this figure, the home contains FLs, SLs, rooftop PV, ESS, and EV. The power consumed by FLs could not be scheduled; however, SLs are integrated in optimization process of HEM to be dispatched within the predetermined intervals by the home owner. The EV is scheduled such that it could be involved in both active and reactive power exchanges. The charging converter
7
of EV is a bidirectional converter which can inject or absorb reactive power to the common coupling point. A similar functionality is attained by ESS. Moreover, it does not encounter with the unavailability issues seen in EV departure. All of these devices are linked to HEM to announce the
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essential information and then get the optimal output commands. The initial state of energy (SOE) of ESS and EV, arrival and departure time of EV, sun irradiance of rooftop PV, operation requirement of SLs, and electricity price signals are denoted as the required input signals.
2.1.Objective Functions The proposed model of HEM consists of two stages. In the first stage, the daily operation cost of
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home is minimized. As mentioned earlier, the obtained minimized cost in this stage is assigned within
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the constraints in the second stage and the objective is to maximize the PF of home-to-grid integration
Daily Operation Cost of Home
M
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point and compensate the reactive power.
In the first stage, the proposed HEM guarantees the minimum daily operation cost of home
ED
according to (1). 144
[ ( P
grid
) ( t
PT
min F
cost
t
t
sold
t ) max 0, Pt
grid
]. t
t 1
(1)
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subject to (3)-(37)
In (1), the first term declares the cost of purchased power from the grid. The developed model
also includes energy selling back to the grid, as denoted by the second term. Energy buy and sell
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prices namely t and t
sold
are supposed not to be equal. Specifically speaking, the selling price
is assumed to be a bit higher than the energy buying price to encourage the home owner to cooperate in power supply process at peak intervals. Note that the “max” function in (1) is a nonlinear term and is linearized in an exact manner in the following.
8
PF Correction
When the minimum operation cost of smart home is attained in the first stage, the second stage is triggered to maximize the PF at the home-to-grid integration point. As clarified, in this process, the minimum operation cost obtained in the first stage is considered as a constraint and through an
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optimal rescheduling of active elements including SLs, ESS, and EV, the proposed HEM maximizes the PF. To this end, the consumed reactive power of home is minimized compared to the absolute value of active power, demonstrated in (2). Note that the absolute function represents a non-linear term and is properly linearized in the following.
max F
P
grid
t
grid
Qt
U
144
PF
(1)-(25) subject to cost cost F F
M
2.2.Components Modeling and Constraints
(2)
A
N
t 1
Active and Reactive Power Balance
ED
Operation scheduling for all devices should be performed such that the active and reactive
PT
power balance should be maintained at each time interval. (3) and (4) are considered.
CC E
Pt grid Pt PV Pt EV Pt ESS Pt EV Pt ESS Pt FL xt ,i PtSL ,i ,
(3)
t
i
Qtgrid QtEV Cap QtESS Cap QtFL xt ,iQtSL ,i ,
t
(4)
i
A
where xt ,i denotes the on/off state of i-th SL at time interval t. Note that 1 represents the “on”
state of each SL and 0 denotes its “off” state. As can be seen in (4), the ESS and EV are scheduled in capacitive mode to compensate the reactive power requirements of home appliances. Besides, the home-to-grid reactive power injection is not allowed in this study. Because, the reactive power
9
injection from the home toward the grid interferes with the Volt/VAr control processes of the network operator and introduces new issues to handle. Hence, this case is not considered; however, the interested readers could go in through this idea as a possible research in future smart home
SC RI PT
interactions. The apparent power of home drawn from or injected to the grid can be restricted by the grid operator in order to avoid the feeder congestion or operation bottlenecks. To do so, (5) is considered.
P Q S
2
grid t
2
grid t
2
,
t
(5)
U
grid
t
Modeling of SLs
N
Scheduling of operation time intervals of SLs is performed by the proposed HEM to attain an
A
optimum operation point for the whole system. Meanwhile, operation constraints of SLs are
x
i ,t
UTi ,
i
t UTi 1
xi , k UTi sui ,t ,
CC E
k t
sui ,t - sdi ,t xi ,t - xi ,t 1,
t 144 UTi 1, i.
t , i.
(7)
(8)
t , i.
(9)
A
sui ,t sdi ,t 1,
(6)
PT
t si
ED
fi
M
presented by (6) to (9).
As an important technical constraint, the operation time of i-th SL should be equal to the up time
(UT) of that device which should be in the allowable time interval declared by the start and finish intervals, represented by si and fi . These requirements are modeled by (6). The operation time
10
intervals of SLs should be consecutive which is modeled by (7). Moreover, startup/shutdown variables and on/off states are mathematically related to each other by (8). Each SL cannot be on and off simultaneously at each time interval. Accordingly, (9) is included. Modeling of EV
SC RI PT
As it is well-recognized, vehicle-to-home (V2H) concept is developed to make benefit of EV opportunities for optimizing the home interactions. The conceptual illustration of the EV power exchange with the home is shown in Fig. 2. The EV can store the energy in its battery and discharge it back to the grid at the required time intervals. Besides, its converter can participate in reactive
U
power exchanges in inductive or capacitive modes. The reactive power transfer only involves the
N
converter of EV and does not pose any burden on its battery. However, the reactive power
A
provision of EV converter restricts the opportunity of active power flow through it. The operation
M
of EV converter on active and reactive power provision is determined by the HEM to optimize the operation of smart home.
ED
EVs operation scheduling depends on its availability in home. In unavailability time intervals no task should be accounted for EVs. The unavailability of EV is specified by the departure and
PT
arrival times represented by tD and t A , respectively. At each time interval, EV could be only in EV
CC E
one of the charging or discharging modes represented by xt
EV
and xt
, respectively. Thus, the
interactions of EV are formulated by (10) to (19). EV
A
xt
xtEV 1,
t
PtEV CREV .xtEV ,
(10)
t
PtEV DE EV DREV .xtEV ,
PtEV PtEV PtEV ,
(11)
t
(12)
t
(13)
11
P Q EV
t
2
EV Cap t
S 2
EV max
2
,
t
(14)
EV SOEtEV SOEinitial , t 1 1
SOE EV ,min SOEtEV SOE EV ,max ,
1 DE EV
.Pt EV .t,
t tD , t tA
(16)
SC RI PT
SOEtEV SOEtEV CE EV .Pt EV .t 1
(15)
t
(17)
SOEtEV SOEREV , t tD
(18)
SOEtEV SOEtEV SOEREV , t tA 1
(19)
D
U
In (10), the EV cannot be simultaneously within the charge and discharge modes. The charging
N
and discharging power of EV battery are restricted by the maximum charge and discharge rates in
A
(11) and (12), respectively. The net active power of EV is calculated by (13). As mentioned earlier,
M
the EV converter is additionally capable to exchange reactive power with AC bus. Therefore, the apparent power should not exceed the converter rating according to (14). The SOE of EV battery
ED
at initial time interval of scheduling is equal to the residual energy from the previous operation day modeled by (15). The SOE of EV battery at intraday time intervals is calculated by (16). The
PT
SOE of battery should be restricted in permissible range denoted by (17). To provide the required
CC E
energy for a specific travel distance, the SOE of battery at departure time should be higher than the required SOE for that travel, satisfied by (18). Eventually, the SOE at the arrival time is calculated by (19).
A
Modeling of ESS
Application of ESS in smart home fulfills the energy storage requirements along with EV. In
the proposed HEM strategy, ESS is modeled within (20) to (27). ESS
xt
xtESS 1,
t
(20)
12
PtESS CRESS .xtESS ,
t
(21)
PtESS DE ESS DRESS .xtESS ,
PtESS PtESS PtESS , ESS
2
ESS Cap t
t
(22)
t
(23)
S 2
2
ESS max
,
t
SC RI PT
P Q
t
(24)
ESS SOEtESS SOEinitial , t 1 1
(25)
SOEtESS SOEtESS CE ESS .Pt ESS .t 1 SOE ESS ,min SOEtESS SOE ESS ,max ,
1 DE ESS
.Pt ESS .t,
t
t
(26)
U
(27)
N
Similar to EV modeling procedure, converter of ESS is also capable of injecting or absorbing
A
reactive power. The important point to be mentioned is that the operation mode of EV and ESS in
M
reactive power provision should be the same. That means the converters of EV and ESS should be in the same mode of either injection or absorption of reactive power. In order to avoid opposite
Q
EV Cap
atQt
CC E
EV Cap t
Q
EV Cap t
Q
A
Q
ESS Cap t
ESS Cap t
EV Cap
(28)
(29)
M
(30)
Q
ESS Cap
atQt
M
1 atQt
QtESS Cap QtESS Cap
Q
EV Cap t
PT
QtEV Cap QtEV Cap
ED
operation mode, (28) to (34) are included in EV and ESS modeling.
(31)
M
ESS Cap
1 atQt
ESS Cap t
(32)
M
(33)
13
EV Cap
atQt
ESS Cap
atQt
(34)
Note that (28)-(30) and (31)-(33) specify the sign of QtEV Cap and QtESS Cap , respectively. If the EV Cap
ESS Cap
atQt
or
SC RI PT
reactive power of EV or ESS has a positive sign, the corresponding binary variable atQt
should be 1 and vice versa. Accordingly, (34) denotes that the sign of reactive power for
EV and ESS should be the same. This issue is in regard of the sign of reactive powers of EV and ESS. This constraint is included to avert the case in which the ESS is in capacitive mode and EV is in inductive mode or vice versa. In other words, one of them generates reactive power and the other
U
one consumes the reactive power which is a bad and wrongly tuned operation condition. The EV
N
and ESS should not interact with each other and only should provide the reactive power requirements
M
to assure a technically-satisfied operation.
A
of the appliances. Accordingly, the sign of EV and ESS reactive powers is assigned to be the same
Modeling of PV
ED
The maximum active power which can be produced by PV unit depends on solar irradiance and
PT
is calculated by (35). The PV active power generation at each time interval should be less than its maximum value stated in (36). Also, the produced power should be less than PV converter capacity
CC E
as denoted by (37). PV
Pt
PV APV I PV
(35)
PV
(36)
PtPV S PV
(37)
A
Pt PV Pt
2.3.Linearization Tricks The developed mathematical model of HEM in (1)-(37) demonstrates nonlinear terms ending to a mixed-integer non-linear programming (MINLP) format. To avert the non-linear nature of the
14
proposed model, (1), (2), (5), (14) and (24) should be linearized. To this end, the following linearization techniques are deployed.
Maximum Function: max(A, B)
SC RI PT
The maximum function in (1) is a discrete nonlinear function. This function can be linearized as follow: A Mamin B A
(38)
min B MaB A
(39)
min amin aB 1 A
U
(40)
N
L max(A,B) A
M
A
L max(A,B) B
L min max(A,B) B MaB
ED
L max(A,B) A Mamin A
(41) (42) (43) (44)
amin A
min
and aB
are the binary variables specifying the minimum of variables A and B by taking
CC E
Here,
PT
L In (38)-(44), the maximum of two variables A and B, max (A,B) , is calculated in a linear format.
the value of 1, respectively. Then, based on (41)-(44), the maximum variable is chosen.
Absolute value
A
The absolute function denotes a discrete nonlinear function which can be linearized as follow.
Pt grid =ΔPt grid -ΔPt grid
(45)
Pt grid =ΔPt grid ΔPt grid
(46)
0 ΔPt grid atP M
(47)
grid
15
0 ΔPt grid 1 atP
grid
M
(48)
P-Q circle
The apparent power limitation of home-to-grid integration point, ESS, and EV converter transfer
SC RI PT
capacity declared in (5), (14), and (24) demonstrate a nonlinear form. To linearize these equations, a trapezoidal method is applied [18]. Here, (49)-(54) represent the linearization process in which subscripts l and r denote the left and right upper corner values of n-th trapezium in PQ coordinate system, respectively. Also n is a binary variable that specifies which trapezium is selected.
Trap
1, 2,..., N Trap
qn ,
Trap
1, 2,..., N Trap
n
Trap
Trap
M n qn M n
n 1
Trap
n,r
(50) (51) (52)
n,l
(53) (54)
PT
n
M
Pn,r Pn,l
Q Q
P Pn,l qn Qn,l Pn,r Pn,l P Pn,l
ED
Qn,r Qn,l
A
n Pn,l pn n Pn,r Qn,l
(49)
U
pn ,
n
Q
N
P
3. Case Studies and Numerical Results
CC E
3.1. Typical Smart Home Specifications The proposed HEM is simulated on a typical smart home which encompasses FLs, SLs, EV,
ESS, and PV units. The consumption data of the FLs is listed in TABLE I whereas three SLs and
A
their operating intervals are listed in TABLE II. Note that the PF values of all appliances are extracted from [19]-[20]. It is of significance to mention that the scheduling time interval is assumed to be 10 minutes based on which a typical day is represented with 24 6 144 intervals.
16
The smart home hosts an EV which its presence is depended on the home owner’s driving behavior. This EV is unavailable between time intervals 45 to 104. In the remaining time intervals, it is parked at the garage and can be participated in home load commitment. In the conducted study,
SC RI PT
the simulated EV is Chevy Volt which its technical data is listed in TABLE III. Moreover, the technical data of ESS are listed in TABLE IV.
A 3 kW rooftop PV is considered to be installed at home. The daily solar irradiance and PV generation is demonstrated in Fig. 3. Besides, the electricity price provided form the external grid is illustrated in Fig. 4 [20].
U
3.2. Case Studies
N
The developed model for HEM is investigated in two case studies. In Case1, only the operation
A
cost of home is optimized. In other words, this case corresponds to the first stage of the proposed
M
HEM where the only objective is to minimize the daily operation cost. In Case2, the HEM is assessed based on the assumptions of the second stage in which the PF of the home-to-grid integration point
ED
is also maximized. In this process, the obtained minimum cost at the first stage is kept constant and
PT
the full utilization of EV and ESS functionalities is performed. The obtained results for both case studies are discussed, comparatively. The proposed MILP HEM strategy is modeled in GAMS®
CC E
and solved based on CPLEX® 12.5 with zero value of relative gap. It is worth mentioning that the resolution time of 10 minute is applied in simulation studies. The charging power profile of EV and ESS are depicted in Figs. 5 and 6. As can be seen, the
A
charging task of EV and ESS is performed at the initial time intervals with lower electricity prices. However, there are not similar discharge patterns for EV and ESS; since the EV is unavailable in some specific time intervals. From the results, the most suitable time intervals for discharging of energy is coincident with peak power intervals with higher electricity price. Charging at off-peak intervals with lower electricity rates and discharging the stored power at peak power intervals averts 17
purchasing the electricity from the external grid with higher rates. Thus, the operation cost reduces. EV and ESS charge/discharge cycles are depicted in Figs. 7 and 8. As can be seen, some differences are noticed comparing the Case1 and Case2. Note that the reactive power provision of EV and ESS
SC RI PT
in Case1 is zero. However, in Case2, EV and ESS are involved in reactive power provision and as can be seen, the charge/discharge cycles of these components differ with those of Case1. Specifically speaking, in Case2, HEM utilizes the remaining capacity of ESS and EV interfacing converter to compensate the reactive power of FLs and SLs, locally. The reactive power provision of EV and ESS are shown in Figs. 9 and 10. As can be seen, the reactive power provision of EV and ESS is
U
increased in peak load time intervals to meet the requirements of the FLs and SLs. This trend
N
influences the active and reactive power exchanges of home with the grid. The active power import
A
from the grid is demonstrated in Fig. 11. As can be seen, in midday time intervals, the imported
M
power is decreased. However, the differences in Case 1 and Case 2 are not so huge. Besides, at the time intervals with higher electricity rates, the home exports active power to the grid, i.e., it sells
ED
back the stored power to the grid to attain higher monetary benefits. A similar graph is provided for
PT
the reactive power exchanges in Fig. 12. The reactive power is recorded at the PCC and relates to the aggregated loads. In Case 1, all of the required reactive power of home appliances is provided
CC E
form the external grid. However, in Case 2, at some intervals, EV and ESS provide a portion of the required reactive power inside the home and the remaining amount is supplied form the external grid. Also, at some other intervals, the reactive power requirement of the home appliances is totally
A
provided from the ESS and EV inside the home. In other words, zero reactive power is provided from the external grid. The imported reactive power from the grid to the home is demonstrated to study the PF of the home at the grid integration point. Fig. 13 depicts the PF obtained at this point. As can be seen, participating of ESS and EV in reactive power provision in Case2 reduces the reactive power burden of the network and hence, enhances the PF profile. The PF is sensibly 18
increased toward unity and it meets the minimum permissible PF triggered in financial penalty calculations by most of the utilities. As explained, in the smart grid concept, the home owners are willing to install small-scale renewable power generation systems. As well, they are equipped with
SC RI PT
EV and ESS in power management of the home. In this situation, at some specific intervals, the active power requirement of home appliances is locally supplied inside the home and a lower active power is transferred from the upstream network at the point of grid integration. Though, all of the required reactive power is supplied from the external grid (Case 1). This situation results in a lower power factor at the home-to-grid integration point which at some intervals, it also reaches to zero.
U
This issue puts a high concern on lower PF of future smart homes and calls the need for efficient
N
HEM systems dealing with reactive power processes, too. In the proposed model and in Case 2 which
A
incorporates the reactive power provision of EV and ESS, a portion of the reactive power of home
M
appliances is satisfied inside the home and the remaining is provided form the external grid. Hence, the PF is increased at the home-to-grid integration point.
ED
The overall daily operation cost is recorded in Table V. It can be inferred that at the second stage,
PT
maximization of the PF does not incur any financial losses and the obtained minimum cost at the first stage remains constant.
CC E
Scheduling of SLs is shown in Fig. 14. As can be seen, these appliances are similarly scheduled at the investigated cases and are turned on at two specific time intervals. The first interval includes PV availability intervals with significant power generation and the second one refers to the intervals
A
with lower electricity price. Eventually, the total operation cost of home is displayed in Fig. 15 for both cases. As can be seen, slight changes are noticed in operation cost; however, the total cost is kept at minimum value in Case1 which is 412.9$. In some specific intervals within which the power production of PV increases or the selling tariff grows, the operation cost is negative and the home exports active power to the grid. 19
6. Conclusions A two stage efficient HEM strategy furnished by SLs, PV, EV, and ESS was developed in this paper. In the first stage, the operation cost of home was minimized while the PF was
SC RI PT
improved in the second stage. To this end, an efficient mathematical model was developed and proper linearization techniques were deployed to avert the non-linear nature of the established model. Deployment of EV and ESS storage capacity on active power exchanges resulted in an optimal charge/discharge patterns and decreased the operation cost of home significantly. Besides, the reactive power provision by EV and ESS converters was properly included in
U
reactive power compensation of home appliances. More interestingly, this feature did not
N
threaten the economic success of the proposed HEM in the first stage and the obtained
A
improvements were only afforded as side-effect opportunities. Accordingly, a significant
M
improvement was noticed in PF correction of the home-to-grid integration point. By this way, lower burden of reactive power is incurred from the home to the grid and the DSO is encountered
ED
a more facilitated operation of grid at the upper levels. This point is an interesting outcome in
PT
smart grid studies dealing with interactions of smart home and distribution system operations. References
CC E
[1] N. G. Paterakis, O. Erdinç, A. G. Bakirtzis and J. P. S. Catalão, “Optimal Household Appliances Scheduling Under Day-Ahead Pricing and Load-Shaping Demand Response Strategies,” IEEE Trans. Ind. Inform., vol. 11, no. 6, pp. 1509-1519, Dec. 2015.
A
[2] K. M. Tsui and S. C. Chan, “Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1812-1821, Dec. 2012.
[3] F. L. Meng and X. J. Zeng, “A Profit Maximization Approach to Demand Response Management with Customers Behavior Learning in Smart Grid,” IEEE Trans. Smart Grid, vol. 7, no. 3, pp. 1516-1529, May 2016.
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[4] I. Dincer and Canan Acar “A review on clean energy solutions for better sustainability,” International Journal of Energy Research, vol. 39, no. 5, pp. 585-606, April 2015. [5] I. Dincer and M.A. Rosen “A worldwide perspective on energy, environment and sustainable development,” International Journal of Energy Research, vol. 22, no. 15, pp. 1305-1321, December 1998.
SC RI PT
[6] F. Y. Melhem, O. Grunder, Z. Hammoudan and N. Moubayed, “Optimization and Energy Management in Smart Home Considering Photovoltaic, Wind, and Battery Storage System With Integration of Electric Vehicles,” Canadian Journal of Electrical and Computer Engineering, vol. 40, no. 2, pp. 128-138, Spring 2017.
[7] A. Mahmood, N. Javaid, M. Asghar Khan and A. Razzaq “An overview of load management techniques in smart grid,” International Journal of Energy Research, vol. 39,
U
no. 11, pp. 1437-1450, September 2015.
N
[8] D. Setlhaolo, X. Xia and J. Zhang, “Optimal Scheduling of Household Appliances for Demand Response,” Electric Power Systems Research, vol. 116, pp. 24-28, November
A
2014.
M
[9] J. H. Yoon, R. Bladick and A. Novoselac, “Demand Response for Residential Buildings
September 2014.
ED
based on Dynamic Price of Electricity,” Energy and Buildings, vol. 80, pp. 531-541, [10]J. Katz, F.M. Andersen, and P.E. Morthorst, “Load-shift Incentives for Household Demand Response: Evaluation of Hourly Dynamic Pricing and Rebate Schemes in a Wind-based
PT
Electricity System,” Energy, vol. 115, pp. 1602-1616, November 2016. [11]X. Li and S. Hong, “User-expected price-based demand response algorithm for a home-to-
CC E
grid system,” Energy, vol. 64, pp. 437–449, Jan. 2014.
[12]L. Jian, H. Xue, G. Xu, X. Zhu, D. Zhao and Z. Y. Shao, "Regulated Charging of Plug-in Hybrid Electric Vehicles for Minimizing Load Variance in Household Smart Microgrid,"
A
in IEEE Trans. Ind. Electron, vol. 60, no. 8, pp. 3218-3226, Aug. 2013.
[13]R. Lamedica, S. Teodori, G. Carbone, E. Santini, “An energy management software for smart buildings with V2G and BESS,” Sustainable Cities and Society, vol. 19, pp. 173-183, 2015.
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[14]Q. Wei, D. Liu, G. Shi, Y. Liu, “Multibattery optimal coordination control for home energy management systems via distributed iterative adaptive dynamic programming,” IEEE Trans. Ind. Electron., vol. 62, no. 7, pp. 4203-4214, Jul. 2015. [15]M. Rastegar, M. Fotuhi-Firuzabad, F. Aminifar, “Load commitment in a smart home,” Applied Energy, vol. 96, pp. 45-54, 2012.
SC RI PT
[16]O. Erdinc, N. G. Paterakis, T. D. P. Mendes, A. G. Bakirtzis, J. P. S. Catalao, “Smart household operation considering bi-directional EV and ESS utilization by real-time pricingbased DR,” IEEE Trans. Smart Grid, vol. 6, no. 3, pp. 1281-1291, May 2015.
[17]A EURELECTRIC paper, “Network Tariff Structure for a Smart Energy System,” available online: http://www.eurelectric.org/media/80239/ 20130409_network-tariffspaper_final_to_publish-2013-030-0409-01-e.pdf
U
[18]A. Hamidi, S. Golshannavaz and D. Nazarpour, “D-FACTS Cooperation in Renewable
N
Integrated Microgrids: A Linear Multi-Objective Approach,” IEEE Trans. Sustainable Energy, 2017, doi: 10.1109/TSTE.2017 .2723163
A
[19]A.S. Pabla, Electric Power Distribution, McGraw-Hill Education, pp. 867-870, 2004.
M
[20]O. Erdinc, “Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households,” Applied Energy,
A
CC E
PT
ED
vol. 126, pp. 142-150, 2014.
22
AC Point of common coupling with grid
Information signals from smart grid operator
Shiftable loads
SC RI PT
HEM Fixed loads
Fig.1. Data and power flow in a smart home.
CE EV
1 P EV DE EV
DE EV
P EV
SL / FL P EV
AC Bus
U
CE EV P EV
M
A
N
Q EV Cap
EV Charger DC/AC Converter
ED 3 2.5 2 1.5 1 0.5 0
1
12 23 34 45 56 67 78 89 100 111 122 133 144 Time interval
Fig. 3. Daily power generation by PV.
A
CC E
PT
Active power (kW)
Fig.2. Conceptual illustration of V2H.
23
External Grid
Electricity price ($/kWh)
5 4 3 2 1 0
SC RI PT
1 12 23 34 45 56 67 78 89 100 111 122 133 144 Time interval
4
2
U
0
-4
N
-2
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139
EV power (kW)
Fig. 4. Electricity price from the grid.
A
Time interval (10 min)
M
ED
4 3 2 1 0 -1 -2 -3 -4
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 Time interval (10 min)
Fig. 6. Charge/discharge cycles of ESS in Case1.
4
2 0 -2 -4
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139
EV power (kW)
A
CC E
PT
ESS power (kW)
Fig. 5. Charge/discharge cycles of EV in Case1.
Time interval (10 min)
24
SC RI PT
4 3 2 1 0 -1 -2 -3 -4
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139
ESS powe r (kW)
Fig. 7. Charge/discharge cycles of EV in Case2.
Time interval (10 min)
U N A
M
3.5 3 2.5 2 1.5 1 0.5 0
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139
Re active power (kVar)
Fig. 8. Charge/discharge cycles of ESS in Case2.
Time interval
ED
3.5 3 2.5 2 1.5 1 0.5 0
1
12 23 34 45 56 67 78 89 100 111 122 133 144 Time interval
Fig. 10. Reactive power provision of ESS in Case2.
A
CC E
PT
Re active power (kVar)
Fig. 9. Reactive power provision of EV in Case2.
25
Active power (kW)
15
Case1 Case2
10 5 0 -5
-10 12
23 34 45 56 67 78 89 100 111 122 133 144 Time interval
SC RI PT
1
Fig. 11. Active power import/export from/to the grid.
Re active power (kVar)
6
Case1 Case2
4 2
U
0
-2
12 23 34 45 56 67 78 89 100 111 122 133 144 Time interval
N
1
M
A
Fig. 12. Reactive power import from the gird.
Powe r factor
1
ED
0.8 0.6 0.4
Case1 Case2
PT
0.2
0
12
23 34 45 56 67 78 89 100 111 122 133 144 Time interval
Fig. 13. PF record at the home-to-grid integration point.
4
Active power (kW)
A
CC E
1
3 2 1
0 1
12
23 34 45 56 67 78 Time interval
89 100 111 122 133 144
Fig. 14. Active power consumption of SLs in Case1 and Case2.
26
25 Case1 Case2
Cost ($)
15 5 -5
-15 1
12
23
34
45
SC RI PT
-25
56 67 78 89 100 111 122 133 144 Time interval
A
CC E
PT
ED
M
A
N
U
Fig. 15. Smart home operation cost in Case1 and Case2.
27
TABLE I FLS SPECIFICATIONS
1.20 2.40 0.22 2.00 1.66 0.08 1.14 0.28 0.20 0.20 0.60
0.93 0.95 0.60 1.00 0.65 0.80 0.90 0.95 0.95 0.80 0.75
06:30-07:00 and 19:30-20:00 19:30-20:00 06:30-07:30 and 19:30-20:30 07:00-07:30 and 20:00-20:30 24 hour 06:30-07:30 and 20:00-22:00 00:00-07:00 and 19:00-24:00 22:00-24:00 20:30-22:00 06:00-07:30 and 19:00-24:00 20:30-21:00
A
Microwave Oven Ventilation fan Kettle Refrigerator Radio Air conditioner TV Desktop computer Lights Vacuum cleaner
Daily working time
SC RI PT
PF
U
Power (kW)
N
Appliance
TABLE II
M
SLS SPECIFICATION
PT
CC E
Clothes dryer Dishwasher Washing machine
Power (kW)
PF
ED
Appliance
3.8 1.32 1.4
1.00 0.70 0.57
EV SPECIFICATIONS Value
EV
SOE
EV ,min
A
SOE
1 hour-once 1 hour-once 1 hour-once
TABLE III
Technical parameter EV ,max
Permitted Interval UT & frequency
Chevy Volt 16 kWh 1.6 kWh
EV
8 kWh
EV
SOER
12 kWh
EV max
3.3 kVA
SOEinitial
S
EV ,max
CR DREV ,max
3.3 kW 3.3 kW
28
si
fi
115 45 45
144 114 114
CE EV , DE EV
0.95
TABLE IV ESS SPECIFICATIONS
SOE
ESS ,max
SOE
ESS ,min
Value
SC RI PT
Technical parameter
10 kWh 1 kWh
ESS
SOEinitial
3 kWh
ESS max
S
CR
DR
3 kW
ESS ,max
ESS
, DE
3 kW
ESS
0.95
A
CC E
PT
ED
M
A
N
U
CE
3 kVA
ESS ,max
29