a multi-objective optimization approach towards a proposed
TRANSCRIPT
energies
Article
A Multi-Objective Optimization Approach towards aProposed Smart Apartment with Demand-Responsein Japan
Yuta Susowake 1,†, Hasan Masrur 1,†, Tetsuya Yabiku 1,†, Tomonobu Senjyu 1,∗,Abdul Motin Howlader 2,†, Mamdouh Abdel-Akher 3,4,† and Ashraf M. Hemeida 5,†
1 Faculty of Engineering, University of the Ryukyus, Senbaru Nishihara-cho, Nakagami 903-0213, Okianwa,Japan; [email protected] (Y.S.); [email protected] (H.M.); [email protected] (T.Y.)
2 Department of Electrical Engineering, University of Hawaii, Manoa, 1680 East-West Road,Honolulu, HI 96822, USA; [email protected]
3 Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt;[email protected]
4 Department of Electrical Engineering, Unaizah College of Engineering, Qassim University,Unaizah 56453, Saudi Arabia
5 Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Sahary,Aswan 51528, Egypt; [email protected]
* Correspondence: [email protected]; Tel./Fax:+81-98-895-8686 (ext.8686)† These authors contributed equally to this work.
Received: 29 October 2019; Accepted: 21 December 2019; Published: 25 December 2019�����������������
Abstract: In Japan, residents of apartments are generally contracted to receive low voltage electricityfrom electric utilities. In recent years, there has been an increasing number of high voltage batch powerreceiving contracts for condominiums. In this research, a high voltage batch receiving contractorintroduces a demand–response in a low voltage power receiving contract, which maximizes theprofit of a high voltage batch receiving contractor and minimizes the electricity charge of residentsby utilizing battery storage, electric vehicles (EV), and heat pumps. A multi-objective optimizationalgorithm calculates a Pareto solution for the relationship between two objective trade-offs in theMATLAB R© environment.
Keywords: smart apartment; photovoltaic; multi-objective optimization; demand–response; real-timepricing; NSGA-II
1. Introduction
The use of renewable energy resources is necessary to generate electricity to help mitigateenvironmental problems. In Japan, the introduction of renewable energies, specifically photovoltaic(PV) systems, is rapidly expanding. However, PV has the disadvantage that its output power fluctuatesdepending on the weather conditions. Furthermore, PV cannot generate electricity at night or early inthe morning due to the absence of the Sun. Therefore, in order to supply electricity even during theperiod when PV cannot generate electricity, the use of battery storage is effective. Currently, manyresearchers are working on a new home energy management system (HEMS) with PV, wind generators(WG), and storage systems. The work in [1] introduced PV coupled with battery banks and proposedenergy consumption models for smart homes using demand–response. The energy scheduling problemof smart houses with PV, diesel generators, and batteries and the unit commitment problem of powergeneration companies were described in [2]. The work in [3] proposed a probabilistic model ofHEMS using a demand–response program, taking into account the uncertainties of EV availability
Energies 2020, 13, 127; doi:10.3390/en13010127 www.mdpi.com/journal/energies
Energies 2020, 13, 127 2 of 14
and renewable power generation. The researchers in [4] compared proportional-integral-derivative(PID) and model predictive control of home air conditioning with PV and demand–response. S.vander Stelt et al. discussed the technical and economic feasibility of combining energy storage systemsand demand-side management, and HEMS scheduled the allocation of energy sent from PV systems,batteries, and grid to meet household power demand [5]. The structure and functional models of smartHEMS that use PV, WG, geothermal energy, and biomass were outlined in [6], where the scheduling ofhome appliances was considered to reduce electricity charges, as well as improve energy efficiency.The work in [7] described the benefits and possibilities of demand–response in smart grids.
It is possible for the EV to play the role of a storage battery during parking at home. Manyresearchers are studying a system that not only powers EVs from homes, but also powers homes fromEVs. The work in [8] proposed a stochastic dynamic programming framework for optimal energymanagement of smart homes with plug-in electric vehicles’ (PEV) energy storage with vehicle-to-grid(V2G), vehicle-to-home (V2H), and grid-to-vehicle (G2V) modes of operation. Ozan Erdinc et al.discussed dynamic pricing and demand–response strategies with bidirectional availability of EVand energy storage systems [9]. The work in [10] proposed an evaluation of a framework for smarthouseholds that have introduced an electric vehicle with an interactive power flow function, an energystorage system, and a small distributed power generation unit. The work in in [11] also dealt withV2H, V2V, and V2G technologies.
On the other hand, there is an increasing number of bulk high voltage traders (aggregators)contracting to receive power from a power company at high voltage and sell it to apartment residentsin Japan. The received power contract at high voltage has the advantage of a low price per kWh.Therefore, the difference between the electricity charge from the apartment and the electricity businessis a benefit for the aggregator.
In this study, a smart apartment model is presented, which maximizes the profit of the bulk highvoltage trader and minimizes the electricity charges of smart apartment residents. NSGA-II is appliedas a multi-objective optimization method in the MATLAB R© environment. This algorithm calculatesPareto solutions of two objective functions.
The remainder of this paper is organized as follows. Section 2 refers to a hot water supply systemusing a smart condominium model and a heat pump using a solar heat collector introduced to the smartapartment. Section 3 refers to real-time pricing applied as a demand response approach. Section 4describes the objective functions and constraints of this study. Section 5 describes the optimizationmethod used in this study. Section 6 represents simulation conditions and simulation results withanalysis. The conclusions are given in Section 7.
2. Smart Apartment Model
Figure 1 shows the smart apartment building model considered in this study [12,13]. Thephotovoltaic system (PV) and the storage battery was introduced into the apartment model [14].The rated power of PV was 20 kW. Storage battery capacity and rated inverter output were 2000 kWhand 20 kW, respectively. In addition, 10 units of the heat pump (HP) with a coefficient of performance(COP) value of 3.5 and solar heat collectors (SC) were introduced in the smart apartment model [15–17].The smart apartment included 100 households.
Figure 2 shows the proposed contract between a smart apartment, an electric utility, and a bulkhigh voltage trader. Bulk high voltage traders receive high voltage power by entering a high voltagecontract with an electric utility. After that, the high voltage power is converted to a low voltage, andthe power is supplied to the smart apartment, which has a low voltage contract. High voltage powersupply contracts have the advantage of low cost. Therefore, the difference between the electricitycharges received from the apartment and the electricity charges paid to the electricity supplier is thebenefit of the bulk high voltage trader.
Energies 2020, 13, 127 3 of 14
PPVtPBt
PHPt
SC
ElectricityHeat
HP
Battery
PIit
DC DC
Power
PLit
DC bus
demandHeat
demand
PIit
DC DC
Power
PLit
DC bus
demandHeat
demand
PIit
DC DC
Power
PLit
DC bus
demandHeat
demand
Infinite bus
AC DC PIt
PIit
DC DC
Power
PLit
DC bus
demandHeat
demand
DC DC
EV
PEVit
Figure 1. The smart apartment building model.
Electric Utility
Bulk High Voltage Trader
Apartment Dwellers
High Voltage Contract
Low Voltage Contract
with Real-Time Pricing
Figure 2. The proposed contract form.
Energies 2020, 13, 127 4 of 14
In addition, bulk high voltage traders apply real-time pricing as a demand–response method toequalize the power load on the apartment. The storage battery, EV, and HP are used to adjust thepower load. By applying real-time pricing, bulk high voltage traders can reduce the basic charge forincoming contracts with utilities. On the other hand, the residents of the apartment will reduce theirelectricity bills by adjusting the power consumption. The reduced electricity charges will be indicatedby the bulk high voltage trader.
2.1. Hot Water Supply System from HP Using a Solar Collector
In this paper, we considered three SCs as an auxiliary heat source for the heat pumps [18]. Thearea of SC, ASC, and SC efficiency, ηSC, were 1.6 m2 and 60%, respectively. The hot water supply systemusing SC can be formulated by Equations (1)–(7) [19]. The characteristic of hot water temperaturedepends on the ambient temperature, solar insolation, and time. The characteristic is obtained asfollows:
dThdt
=Qh
1000Aw(1)
dQhdt
= −αh(Th − T) (2)
where Th (◦C) is the water temperature in the storage tank, Qh (J) denotes the quantity of heat in thetank, Aw (`) is the capacity of the storage tank, αh is the specific heat of the water, and T (◦C) indicatesthe ambient temperature. The amount of heat from solar irradiation is obtained by the followingequation:
Qa = ηSC IanASC (3)
where Ia (J) and n are the solar irradiation and the number of SC panels, respectively. The heat loss bysupplying hot water Qtl (cal), the added heat by the city’s water supply Qsw (cal), the amount of thehot water supply Atl(l), the amount of the city’s water supply Asw (L), and the heat from SC Qe (J) arecalculated as follows:
Qtl = 1000AtlTh (4)
Qsw = 1000AswTw (5)
Atl = Asw =Tl − Tw
Th − TwAl (6)
Qe = 1000Aw(Te − Th) (7)
where Tl (◦C) is the temperature of the hot water supply, Tw (◦C) stands for the temperature of thecity’s water supply, Al (L) indicates the amount of the hot water supply, and Te (◦C) is the targettemperature.
3. Demand–Response Method
In this section, the demand–response method used in this study and the modeling of the loadresponse to price are described. Section 3.1 describes the real-time pricing of the demand–responsemethod used in this study. Section 3.2 describes the load response modeling.
3.1. Overview of Demand–Response
Demand–response is a mechanism where end-users change their pattern of electricityconsumption to cope with the variation of electricity rates implied by the utility, or they can lower theirpower usage in case of an emergency such as system imbalance, or high electricity market price bybeing involved in schemes like incentive pricing or new tariffs [7]. If power generation is performedwith a high cost power source such as thermal power generation during peak hours of power demand,
Energies 2020, 13, 127 5 of 14
it may be possible to suppress power generation with a high cost power source by suppressing powerdemand through demand–response.
There are three ways to change the use of electricity in demand–response by the participation ofconsumers. These are reducing their energy consumption through load curtailment strategies, movingenergy consumption to a different time period, and using on site standby generated energy, thuslimiting their dependence on the main grid. The load reduction strategy is achieved by lowering thebrightness level of the room lighting or by setting the air conditioner temperature appropriately. Themovement of energy consumption is achieved by moving energy demand from a period of higherpower costs to a period of lower costs, such as pre-cooling a building. Limiting the dependency on themain grid can be achieved by using the power generated on site, utilizing storage technology, stoppingindustrial facilities at night, or moving a part of production to other industrial facilities.
On the other hand, consumers can participate in the demand–response program through theaggregator. In addition, if the user is provided with sufficient incentives, the user can adjust the usageto reduce the peak-to-average ratio of load demand or minimize energy costs.
3.2. Modeling of Real-Time Pricing
In this study, real-time pricing is used as a demand–response method. The demand–responseequation is based on the sigmoid function equation. The equation for the real-time price is shownas follows:
CLt =w
1 + exp {d× (PLa − PLt − k)} + l (8)
where PLa and PLt are the average load power of the apartment (kW) and the total load power of theapartment (kW), respectively. Moreover, w, d, k, and l are the parameters of the real-time pricingwhere wis the difference between the current power demand and the average power demand, d isthe magnitude of the sigmoid function slope, k is the average power demand, and l is the averageelectricity charges. The electric power company can adjust these parameters to lead load demand. Inthis study, these parameters were optimized in order to maximize the profit of the trader and minimizethe electricity charges for the consumers.
The demand–response characteristics of each dweller are illustrated in Figure 3. The load wasadjusted by changing the price depending on demand–response. The load fluctuation became smallerafter the adjustment. In this study, it was assumed that the power demand–response of each dwellerwas based on each characteristics in Figure 3. From Figure 3, the load reduced when the electricityprice was high and increased when the electricity price was low.
10 15 20 25 30
−0.2
−0.1
0
0.1
0.2
Electricity price [Yen/kWh]
Load
var
iati
on [
kW
h]
Figure 3. Function of power response.
4. Formulation of Objective Functions and Constraints
In this paper, it was assumed that the electric power demand and hot water consumption ofapartment dwellers and the output power from PV for a full day could be predicted. The multi-objectiveoptimization problem that was formulated in this study had two objectives. The first objective wasto minimize the total electric charges of apartment dwellers. The second objective was to maximizethe profit of the trader of bulk high voltages. The simulation period was assumed to be one day
Energies 2020, 13, 127 6 of 14
because it was difficult to create one month demand data for HP and EV, and the calculation time waslong in long term simulation. In this study, to formulate the problem as a two objective minimizingoptimization problem, the profit of the trader was reversed. The multi-objective optimization problemis defined as follows:
• The objective functions are:
min Mday =T
∑t∈T{PLtCLt + PEVrtCEVrt − PItCIt − PImCIm} (9)
min CM =T
∑t∈T{PLtCLt} (10)
where: Mday: profit of trader for a full day (Yen), CLt: real-time price (Yen/kWh), PEVrt: consumptionpower of EV (kW), CEVrt: price of EV consumption (Yen/kWh), PIt: interconnection power flow (kW),CIt: price of purchasing power in high voltage contract (Yen/kWh), PIm: maximum of interconnectionpower flow (kW), CIm: basic fees on power receiving contract with electric utility (Yen/kW), and CM:total electricity charges of dwellers for a full day (Yen).
These functions were subjected to following conditions
(i) The total amount of load power variation constraint:
|∆UL| < 0.05×UL (11)
(ii) The amount of load power variation for each dweller constraint:
|∆Uln| < 0.1×Uln (12)
(iii) The upper/lower bound of the real-time price constraint:
10 < CL < 35 (13)
(iv) The profit of the trader constraint:Mday < 0 (14)
(v) The parameters of the real-time pricing constraint:
w > 0, d > 0 (15)
(vi) The adverse power currents constraint:Plit ≥ 0 (16)
(vii) The demand–response peak constraint:
PRm ≤ PLm (17)
(viii) SOC constraint for a full day:0.2× ξBm ≤ ξBt ≤ ξBm (18)
(ix) SOC constraint at 24:00:ξB(t=0) ≤ ξB(t=24) (19)
(x) Active power of fixed battery constraint:
|PBt| ≤ PBm (20)
Energies 2020, 13, 127 7 of 14
(xi) SOC of EV constraint for a full day:
0.2× ξEVm ≤ ξEVt ≤ ξEVm (21)
(xii) SOC of EV constraint at 24:00:ξEV(t=0) ≤ ξEV(t=24) (22)
(xiii) Active power of EV constraint:|PEVt| ≤ PEVm (23)
(xv) Interconnection point power flow constraint:
PIm ≤ PLm (24)
where: UL: the amount of load power in the apartment for a full day (kWh), ∆UL: variationof the amount of load between before and after the demand–response (kWh), Uln: amount ofpower consumption of dweller n for a full day (kWh), ∆Uln: variation of the amount of powerconsumption between before and after the demand–response of dweller n (kWh), Plnt: the loadpower of dweller n after demand–response (kW), PLm: maximum load power of the apartmentafter demand–response (kW), PPm: predicted maximum load power of the apartment (kW), ξBt:SOC of a fixed battery (%), ξBm: maximum SOC of a fixed battery (%), PBt: active power of a fixedbattery (kW), PBm: maximum active power of a fixed battery (kW), ξEVt: SOC of EV (%), ξEVm:maximum SOC of EV (%) (100%), PEVt: active power of EV (kW), PEVm: maximum active powerof EV (kW).
5. Optimization Method
In this study, there were two objective functions: minimization of electricity costs for condominiumcustomers and maximization of profits for high voltage bulk traders. Because there were twoobjective functions, the non-dominated classification genetic algorithm-II (NSGA-II) was used asa multi-objective optimization method [20–22]. The algorithm of NSGA-II is described in Section 5.1.
5.1. Algorithm of NSGA-II
In NSGA-II, two independent populations are used, the saving population Ptand the populationQt for performing searches using genetic operations such as crossover and mutation. The populationPt storing non-dominated individuals is the parent population, and the population Qt will be used forsearching the child population. The flow of the NSGA-II algorithm is shown as follows.
Step 1: Combine the parent population and the child population to generate Rt = Pt ∪ Qt Performnon-dominated sorting on Rt, and classify all individuals according to the front.
Step 2: Generate a new population Pt = ϕ. Let variable i = 1. Execute Pt+1 and i = i + 1 until|Pt+1|+ |Fi| < N is satisfied.
Step 3: Perform congestion degree sorting, and add N − |Pt+1| individuals, which were the mostdiverse, to Pt+1.
Step 4: Based on Pt+1, create a new child population Qt+1 using crowded tournament selection,crossover, and mutation.
Thus, in NSGA-II, the top N individuals of the population Rt, which is a combination of theparent population Pt and the child population Qt, are selected to be the parent-child body Pt+1 ofthe next generation. In addition, the search individual Qt is selected from the parent individual Pt
using congestion tournament selection, and a search using genetic operations is performed usinga superior parent individual Pt. At this time, storing Pt and Qt separately prevents the loss of the
Energies 2020, 13, 127 8 of 14
excellent solution found in the search. A conceptual diagram of updating the parent population Pt ofNSGA-II is shown in Figure 4.
Non-dominated
Sorting
Crowding
distance
sorting
F1
F2
F3
Rejected
Rt
Qt
Pt
Pt+1
Figure 4. Outline of NSGA-II.
6. Simulation Analysis
In this section, the simulation conditions and simulation results of this study are described. Thesimulation conditions are described in Section 6.1, and the simulation results of this study are describedin Section 6.2.
6.1. Simulation Conditions
In this study, a sunny and a cloudy day were considered. In addition, simulations are performedassuming that the daily EV usage, the output of the solar power generation system, and the amount ofhot water supplied from each hot water tank could be predicted in advance. Figures 5–7 show thedaily EV usage, photovoltaic power generation, and hot water supply, respectively. Ten EVs wereinstalled in the apartment building. In this study, it was assumed that the condominium residents paid50 yen/kWh. The total load of the entire apartment other than the assumed controllable load is shownin Figure 8. The total power consumption of the load in Figure 8 was 1000 kWh. The electricity chargesfor a conventional electricity contract (low voltage) were 25 yen/kWh. Therefore, if a conventionalelectricity contract were agreed upon, the total electricity charges would be 25,000 yen. This should benoted in comparison with the proposed contract. Otherwise, the power rate over time at high voltagewas assumed, as shown in Figure 9. In this study, the NSGA-II population size was set to 200 and thenumber of generations to 15,000.
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
0.5
1
Pow
er c
onsu
mpti
on
of
EV
[kW
]
Time t [hour]
Figure 5. Consumption of EV.
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
5
10
15
20
PP
Vt [
kW
]
Time t [hour]
Figure 6. PV output power.
Energies 2020, 13, 127 9 of 14
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
200
400
600
800
Atl [
L]
Time t [hour]
Figure 7. Consumption of hot water.
0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240
20
40
60
Time t [hour]
Load d
em
and [
kW
]
Total of load demand [kW]
Figure 8. Predicted load demand.
0 5 10 15 200
10
20
30
Time t [hour]
Ele
ctri
city
pri
ce [
Yen
/kW
h]
Figure 9. Electricity price for high voltage receiving.
6.2. Simulation Results
Figure 10 shows a Pareto solution calculated by NSGA-II. As shown in Figure 10, the totalelectricity bill for all solutions was less than 25,000 yen. In addition, bulk high voltage traders wererecognized to be profitable with all solutions. The real-time price for all solutions is shown in Figure 11.To level load demand every time, traders need to apply real-time pricing and perform peak load andbottom-up by using fixed batteries. According to Figure 11, the real-time price would be higher if theload demand was high. Figure 12 shows the simulation results of Solution (A). Figure 12a shows thereal-time price as a curve. The load demand before demand–response is also shown as a bar graphin Figure 12a. In Figure 12a, the real-time price will be higher if the load demand is high. The loaddemand after demand–response is shown as a curve in Figure 12b. According to Figure 12b, it can beconfirmed that the load demand leveled. Figure 12c shows the electricity bill for each resident afterdemand–response. It can be observed that all residents could reduce their fees. Figures 12d,e showthe load demand of two dwellers before and after the demand–response. One gained the greatestprofit and the other gained the least profit by demand and response. In Figure 12d, the load demanddecreased if the real-time price was high. In contrast, Figure 12e shows little difference before andafter the demand–response. Figure 12f,g shows the power consumption from HP and the temperatureof hot water in the storage tank, respectively. According to Figure 12f, most HPs operated duringthe daytime to consume the output power from the PV system. In addition, the temperature of hotwater in the storage tank rose with the operation of HP. Figure 12h,i shows the state of charge (SOC) ofthe fixed battery and the SOC of the EV. In Figure 12h, the fixed battery charged in the morning anddischarged during the day. As shown in Figure 12i, due to the constraints, all EVs would be charged tomore than 50% by 24:00.
Energies 2020, 13, 127 10 of 14
-7000 -6000 -5000 -4000 -3000 -2000 -1000 01.8
1.85
1.9
1.95
2
2.05
2.1
2.15
2.2
2.25
2.3 x 104
Profit of Bulk high voltage utility [Yen]
Elec
trici
ty c
harg
es [Y
en]
(A)
Figure 10. Pareto solution.
0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240
20
40
60
Load
dem
and
[kW
]
Natural Load [kW]
0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324
10
20
30
40
50
Time t [hour]
CLt[Y
en/k
Wh]
Prices [Yen/kWh]
Figure 11. Predicted load and real-time prices.
Figure 12j shows the transmitted power from the grid. Compared to the natural Load, thedifference between the peak (highest load demand) and valley (lowest load demand) was smaller. Theinterconnection power flow was leveled to perform peak-cut.
Figure 13 shows the Pareto front when there was almost no power generated by PV. FromFigure 13, the optimum point was determined as Point (B). The profit of bulk high voltage traders in(B) was 1913 yen, and the electricity bill was 21,630 yen. In this case, the profits of bulk high voltagetraders were decreasing compared to Point (A) of Figure 10. The simulation results for Solution (B) areshown in Figure 14a–c. The EV and storage battery were discharged at night.
The costs for the consumer and the profits of the aggregator (utility) are introduced in Table 1. Itwas evident that Solution (A) was better than Solution (B), which means if PV power output were less,then the bulk voltage supplier authority would lose its profit, and consequently, electricity chargeswould be higher.
Table 1. Profit vs. cost representation of two Pareto solutions.
Profit/Cost Solution (A) Solution (B)
Profit of Bulk voltage utility (Yen) 4275 1913
Consumer Cost (Electricity charges) (Yen) 19,800 21,630
Energies 2020, 13, 127 11 of 14
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
20
40
60
Lo
ad
dem
and
[k
W]
Natural Load [kW]
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
10
20
30
40
50
Time t [hour]
CL
[Yen
/kW
h]
Prices [Yen/kWh]
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
5
10
15
PP
Ht [
kW
]
Time t [hour]
(a) Electricity prices for the consumer. (f) HP output power.
0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240
20
40
60
Lo
ad d
em
an
d [
kW
]
Time t [hour]
Natural Load [kW]
Modified Load [kW]
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 2430
40
50
60
70
80
Tt
[°C
]
Time t [hour]
(b) Modified load. (g) Temperature of the water.
1 10 20 30 40 50 60 70 80 90 100100
150
200
250
300
350
Housholds nElec
trici
ty c
harg
es [Y
en]
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
50
100
ξB
t [%
]
Time t [hour]
(c) Charges for consumers. (h) SOC of fixed battery.
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
0.5
1
Time t [hour]
Load
dem
and [
kW
]
After the Demand-Response
Before the Demand-Response
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
50
100
ξE
Vt [
%]
Time t [hour]
(d) Modified load with higher benefit. (i) SOC of EVs.
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
0.5
1
Load
dem
and [
kW
]
Time t [hour]
After the Demand-Response
Before the Demand-Response
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
20
40
60
80
PIt [
kW
]
Time t [hour]
(e) Modified load with lower benefit. (j) Power flow.
Figure 12. Simulation results with (A).
Energies 2020, 13, 127 12 of 14
-2500 -2000 -1500 -1000 -500 0Profit of Bulk receiving high voltage utility [Yen]
2
2.05
2.1
2.15
2.2
2.25
2.3El
ectri
city
cha
rges
[Yen
]10 4
(B)
X:-1913Y:2.163e+04
Figure 13. Pareto solution for reduced PV power output.
0 10 20Time t [hour]
0
20
40
60
80
100
[%]
Bt
(a)
0 10 20Time t [hour]
0
20
40
60
80
100
[%]
EVt
(b)
0 10 20Time t [hour]
0
20
40
60
80PIt
[kW
]
(c)
Figure 14. Simulation results with (B): (a) SOC of fixed battery; (b) SOC of EVs and (c) power flow.
7. Conclusions
In this study, smart condominiums were proposed in which high voltage collective powerreceivers could reduce the electricity charges when they entered into high voltage power receivingcontracts with electric power providers. This was done by leveling the interconnection power flowbetween the condominium and the power system. In the proposed smart condominium, the highvoltage power receivers could optimize the power flow at the interconnection point by optimizing theoperation of storage batteries, EVs, and heat pumps installed in the condominium and further reducethe electric power purchased from the electric power company. The profits obtained by leveling theinterconnection power flow through the optimal operation of demand–response and controllable loadwould increase the profits of the high voltage power receiver and reduce the electricity charges of theentire smart condominium. However, there was a trade-off between increasing profits for high voltagebulk power receivers and reducing electricity charges for the entire smart condominium.
Therefore, in this study, NSGA-II, which is a multi-objective optimization algorithm, was used todetermine the real-time price to be presented to the apartment dwellers by the high voltage collectivepower receiver and to plan the optimal operation of the controllable load. From the results of theoptimal operation plan, the Pareto solution was calculated for the profit of the high voltage bulk powerreceiver and the total amount of electricity charges for the entire apartment. Based on the calculatedPareto solution, it was shown that the profit of the high voltage bulk power receiver and the electricity
Energies 2020, 13, 127 13 of 14
charges of the entire apartment could be adjusted by the optimum operation of demand–response andcontrollable load. Furthermore, PV generated power could benefit both the utility and consumers.
Author Contributions: Conceptualization, T.S.; formal analysis, Y.S., H.M., and T.Y.; methodology, Y.S., T.Y., andH.M.; resources, T.S.; supervision, T.S.; writing, original draft, Y.S. and H.M.; writing, review and editing, H.M.,A.M.H., M.A.-A., and A.M.H. All authors read and agreed to the published version of the manuscript.
Funding: This research received no external funding
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Ima, O.E.; Sun, Y.; Wang, Z. Optimized energy consumption model for smart home using improveddifferential evolution algorithm. Energy 2019, 172, 354–365.
2. Rahmani-Andebili, M.; Shen, H. Price-Controlled Energy Management of Smart Homes for MaximizingProfit of a GENCO. IEEE Trans. Syst. Man Cybern. Syst. 2017, 49, 697–709.
3. Shafie-khah, M.; Siano, P. A Stochastic Home Energy Management System considering Satisfaction Cost andResponse Fatigue. IEEE Trans. Ind. Inform. 2018, 14, 629–638.
4. Godina, R.; Rodrigues, E.M.G.; Pouresmaeil, E.; Matias, J.C.O.; Catalão, J.P.S. Model Predictive Control HomeEnergy Management and Optimization Strategy with Demand Response. Appl. Sci. 2018, 8, 1026–1044.
5. van der Stelt, S.; AlSkaif, T.; van Sark, W. Techno-economic analysis of household and community energystorage for residential prosumers with smart appliances. Appl. Energy 2017, 209, 266–276.
6. Zhou, B.; Li, W.; Chan, K.W.; Cao, Y.; Kuang, Y.; Liu, X.; Wang, X. Smart home energy management systems:Concept, configurations, and scheduling strategies. Renew. Sustain. Energy Rev. 2016, 61, 30–40.
7. Siano, P. Demand response and smart grids—A survey. Renew. Sustain. Energy Rev. 2013, 30, 461–478.8. Wu, X.; Hu, X.; Yin, X.; Moura, S.J. Stochastic Optimal Energy Management of Smart Home with PEV Energy
Storage. IEEE Trans. Smart Grid 2018, 9, 2065–2075.9. Erdinc, O.; Paterakis, N.G.; Mendes, T.D.P.; Bakirtzis, A.G.; Catalão, J.P.S. Smart Household Operation
Considering Bi-Directional EV and ESS Utilization by Real-Time Pricing-Based DR. IEEE Trans. Smart Grid2014, 6, 1281–1291.
10. Erdinc, O. Economic impacts of small-scale own generating and storage units, and electric vehicles underdifferent demand–response strategies for smart households. Appl. Energy 2014, 126, 142–150.
11. Liu, C.; Chau, K.T.; Wu, D.; Gao, S. Opportunities and Challenges of Vehicle-to-Home, Vehicle-to-Vehicle,and Vehicle-to-Grid Technologies. Proc. IEEE 2013, 101, 2409–2427.
12. Tanaka, K.; Uchida, K.; Ogimi, K.; Goya, T.; Yona, A.; Senjyu, T.; Funabashi, T.; Kim, C,-H. Optimal Operationby Controllable Loads for Smart Apartment House. IEEE Trans. Smart Grid 2011, 2, 438–444.
13. Tanaka, K.; Yoza, A.; Ogimi, K.; Yona, A.; Senjyu, T. Optimal operation of DC smart house system bycontrollable loads based on smart grid topology. Renew. Energy 2012, 39, 132–139.
14. Shimoji, T.; Tahara, H.; Matayoshi, H.; Yona, A.; Senjyu, T. Comparison and Validation of Operational Costin Smart Houses with the Introduction of a Heat Pump or a Gas Engine. Int. J. Emerg. Electr. Power Syst.2015, 16, 59–74.
15. Yoza, A.; Uchida, K.; Yona, A.; Senju, T. Optimal Operation Method of Smart House by Controllable Loadsbased on Smart Grid Topology. Int. J. Emerg. Electr. Power Syst. 2013, 14, 411–420.
16. Shimoji, T.; Tahara, H.; Matayoshi, H.; Yona, A.; Senjyu, T. Optimal Scheduling Method of ControllableLoads in DC Smart Apartment Building. Int. J. Emerg. Electr. Power Syst. 2015, 16, 579–589.
17. Uchida, K.; Senjyu, T.; Urasaki, M.; Yona, A. Installation Effect by Solar Pool System Using Solar InsolationForecasting. In Proceedings of the 2009 Annual Conference of Power & Energy Society, Seoul, Korea,26–30 October 2009; Volume 25, pp. 7–12.
18. Yoza A.; Uchida K.; Yona A.; Senjyu T. Optimal operation of controllable loads in DC smart house withEV. In Proceedings of the 2012 International Conference on Renewable Energy Research and Applications(ICRERA), Nagasaki, Japan, 11–14 November 2012.
19. Yoza, A.; Abdul, M.H.; Uchida, K.; Yona, A.; Senjyu, T. Optimal scheduling method of controllable loads insmart house considering forecast error. In Proceedings of the 2013 IEEE 10th International Conference onPower Electronics and Drive Systems (PEDS), Kitakyushu, Japan, 22–25 April 2013.
Energies 2020, 13, 127 14 of 14
20. Deb, K. Multi-Objective Optimization using Evolutionary Algorithms; Jhon Wiley & Sons: 2001; pp. 239–253.21. Shadmand, M.B.; Balog, R.S. Multi-Objective Optimization and Design of Photovoltaic-Wind Hybrid System
for Community Smart DC Microgrid. IEEE Trans. Smart Grid 2014, 5, 2635–2643.22. Kamjoo, A.; Maheri, A.; Dizqah, A.M.; Putrus, G.A. Multi-objective design under uncertainties of hybrid
renewable energy system using NSGA-II and chance constrained programming. Int. J. Electr. Power EnergySyst. 2016, 74, 187–194.
c© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).