Towards Sustainable EnergyEfficient Communities Based on a Scheduling Algorithm
Abstract
:1. Introduction
2. Related Work
3. System Model
3.1. Consumer System Design
 Fixed consumption (kWh) when appliance ${a}_{ij}$ is in standby status
 Consumption (kWh) when ${a}_{ij}$ is on
 Duration (hours/minutes) of the planned operation of appliance ${a}_{ij}$ in the next day
 Point in time (hour, e.g., 8am) of preferred start of appliance ${a}_{ij}$ activation
 Point in time (hour, e.g., 12pm) of preferred end of appliance ${a}_{ij}$ operation
3.2. Aggregator System Design
Algorithm 1 Demand Calculation Function ($\mathcal{DCF})$ 

Algorithm 2 RR strategy 

3.3. Proposed Algorithm: A Fair Division Game
 (A)
 The RR principle, known from other fields such as network scheduling and processor queuing, is based on a process/game/technique, where each task/person/device takes an equal share of something in turn. The RR scheduling can allocate the available electricity from renewables both simple and fairly among the Consumers/ Appliances, because (1) the consumers’ number is known and fixed and (2) the reallocation process is centralised by the Aggregator which, starting on its own, will satisfy the demand of the Consumers/Appliances in a periodically repeated order. We include pseudocode of our algorithm’s main function in the round robin strategy, being the rest pseudocodes similar with exception of the player turn selection on Algorithm 2line 3. RR results in max–min fairness if the Consumers/Appliances’ demands are equally sized; otherwise, fair queuing that establishes a fair share size would be desirable.
 (B)
 A random RR scheduling: A similar process as in A), though the election of first Consumer is random.
 (C)
 A pickingsequence has several merits as a fair division protocol [31]. Assuming that each agent has a (private) ranking over the set of objects, the allocator must find a policy (i.e., a sequence of agents that maximises the expected value of some social welfare function). Moreover, picking sequences are a natural way of allocating (indivisible) items to agents in a decentralised manner: at each stage, a designated agent chooses an item among those that remain available. The goal of the method is to identify the fairest sequence.
 (D)
 A random process could, or could not, introduce efficiency (no other “random” assignment dominates) in the aforementioned methods while keeping them Paretoefficient, envyfree and giving good approximation to the social welfare. Efficiency in terms of computational time is also at stake.
4. System Validation
4.1. Optimisation Algorithms Used
 (i)
 Simulated Annealing (SA) [32] finds a local minimum solution for our Algorithm 1 ($\mathcal{DCF}$) starting at an initial operation time ${t}_{sched}^{i}$. As explained in Algorithm 3, SA starts generating trial point based on current estimates and evaluates the function by accepting a new value generated after $\mathcal{T}$ parameter is set. The solution must consider the $[{t}_{beg}^{i},{t}_{end}^{i}]$ time constraints. ${t}_{sched}^{i}$ can randomly generate and filter by $\mathcal{L}$. In case of better $\mathcal{D}$, the original one ${\mathcal{D}}^{\prime}$, ${t}_{sche{d}^{\prime}}^{i}$ could be accepted as better solution if ${\mathcal{D}}^{\prime}$ is worst than $\mathcal{D}$. After the internal counter reaches its threshold, $\mathcal{T}$ is cooled down and reselect the best solution again with the reset counter.
Algorithm 3 Optimisation based on SA algorithm  1:
 Let $\mathcal{T}$> 0 as initial parameter
 2:
 Let $\mathcal{N}$($\mathcal{T}$) as maximum number of iterations
 3:
 while stop criterion has not been met do
 4:
 Randomly generate a fasible solution ${t}_{sched}$
 5:
 Evaluate ${t}_{sched}^{i}$, $\mathcal{D}$ = f(${t}_{sched}^{i}$)
 6:
 n = 1
 7:
 while while n $<=$ $\mathcal{N}$($\mathcal{T}$) do
 8:
 Generate solution ${t}_{sche{d}^{\prime}}^{i}$ based on ${t}_{sched}^{i}$
 9:
 Evaluation of ${t}_{sche{d}^{\prime}}^{i}$; ${\mathcal{D}}^{\prime}$ = f(${t}_{sche{d}^{\prime}}^{i}$); $\delta $ = f(${t}_{sche{d}^{\prime}}^{i}$)–f(${t}_{sched}^{i}$)
 10:
 if f(${t}_{osi}^{\prime}$) < f(${t}_{sched}^{i}$) then
 11:
 ${t}_{sched}^{i}$ = ${t}_{sche{d}^{\prime}}^{i}$
 12:
 else
 13:
 if$\delta $ >= 0 and u< exp((f(${t}_{sche{d}^{\prime}}^{i}$)–f(${t}_{sched}^{i}$))/$\mathcal{T}$) then
 14:
 ${t}_{sched}^{i}$ = ${t}_{sche{d}^{\prime}}^{i}$
 15:
 end if
 16:
 end if
 17:
 n = n+1
 18:
 end while
 19:
 $\mathcal{T}$ reduction and update ${t}_{sched}^{i}$ at each reduction
 20:
 end while
 (ii)
 Genetic algorithm (GA) [33] is identified as a method mainly used to solve optimisation problems based on a natural selection process similar to biological evolution. GA finds an optimal operative time from our Algorithm 1 ($\mathcal{DCF}$) for the ${\mathcal{A}}_{i}$ variables. As explained in Algorithm 4, GA can find a solution beginning with random population of points. GA repeatedly modifies a population of individual solutions. At each step, GA produces a next generation population based on a randomly selection of individuals from the current population. After that, the population turns into an optimal solution. The evaluation number is increased when the method finishes by calculating one generation $\mathcal{P}$. Each generation is a feasible solution for the appliance scheduling (${t}_{sched}^{i}$ per appliance). In the evaluation stage, the best solution ${t}_{sched}^{i}$, which has the lowest demand, is inserted to the best solution set. Mutation and crossover operators are selected to generate the next evaluation from the current generation. The mutation operator randomly shifts the scheduled start times of some appliances in order to generate newly solutions that may have a better result in demand efficiency. They are screened with the constraints to filter out the infeasible ones. The crossover driver swaps scheduled ${t}_{sched}^{i}$ under feasible solutions.
Algorithm 4 Optimisation based on GA algorithm  1:
 Generate Solutions. Build a set of PopSize $\mathcal{P}$ solution
 2:
 Reformulation of solutions. Selection of a local search method to each solution in $\mathcal{P}$
 3:
 while number of evaluations < MaxEval do
 4:
 ${t}_{sched}^{i}$ introduction to P. Evaluation of solution in $\mathcal{P}$ and update
 5:
 Probability of survival based on the quality of the solution
 6:
 $\mathcal{P}$ solution is partially selected to apply the mutation and crossover operation
 7:
 Number of evaluation ++
 8:
 Constraint validate $\mathcal{P}$ for each ${t}_{sched}^{i}$. Discard solutions which are disqualified
 9:
 end while
 (iii)
 Pattern Search (PS) [34] polls the values around the current point and determines the direction that will minimise our Algorithm 1 ($\mathcal{DCF}$) starting at an initial operation time ${t}_{sched}^{i}$. For each possible direction, an all linear combination of the current position is created, and each pattern is multiplied by the size of the mesh to obtain a new one. As presented in Algorithm 5, PS investigates nearest neighbourhood of a possible solution always in the range of lower and upper bounds $[{t}_{beg}^{i},{t}_{end}^{i}]$ for each appliance. This solution seeks to find a better one. A failure improvement generation by neighbours ($\mathcal{L}$ and $\mathcal{D}$) would reduce the search step ($\Delta $). Search finishes when the step gets sufficiently short, ensuring the convergence to a local minimal overconsumption.
Algorithm 5 Optimisation based on PS algorithm  1:
 Initialise predefine default search step ${\Delta}_{0}$; ${t}_{sched}^{i}$ and $\Delta $ = ${\Delta}_{0}$
 2:
 while Termination condition not reached do
 3:
 init current solution $\mathcal{D}$= (${t}_{sched}^{i}$+$\mathcal{L}$*$\Delta $)
 4:
 Evaluate nearest neighbours in $\mathcal{D}$
 5:
 if betters in $\mathcal{D}$ then
 6:
 Update the current solution to the best neighbour in $\mathcal{D}$; $\Delta $ = ${\Delta}_{0}$
 7:
 else
 8:
 Search step reduction $\Delta $ = ${\Delta}_{0}/2$
 9:
 end if
 10:
 end while
 (iv)
 Particle Swarm Optimisation (PSO) [35] is a stochastic search method and simulates the social behaviour of particles used to find parameters that minimise a given objective. The optimisation determines the minimum value and the best location evaluating our Algorithm 1 ($\mathcal{DCF}$) through iterations.
Algorithm 6 Optimisation based on PSO algorithm 

4.2. Performance Analysis
5. Technical Considerations: Communication, Security and Hardware
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
$\mathcal{N}$  Consumer number 
${\mathcal{A}}_{i}$  Appliance number 
i  Consumer identifier 
j  Appliance index 
${a}_{ij}$  Consumer i’s appliance identifier 
t  Certain time 
$\mathcal{RW}$  24hour supply vector from renewables 
${t}_{beg}$  Earliest start time appliance 
${t}_{end}$  Latest final time appliance 
${t}_{sched}$  Scheduled start time of appliance 
$\mathcal{D}$  Consumer demand 
$v\mathcal{D}$  Variable demand 
$f\mathcal{D}$  Fixed demand 
$CF$  Consumer Flexibility 
$\mathcal{L}$  Duration of the planned operation of appliance ${a}_{ij}$ in the next day 
$SH$  Smart Home 
$HEMS$  Home Energy Manager System 
$HAN$  Home Arena Network 
$NAN$  Neighbour Area Network 
$WAN$  Wide Area Network 
$IoT$  Internet Of Things 
$ICT$  Information and Communication Technologies 
$SG$  Smart Grid 
$DSM$  Demand System Manager 
$MILP$  Mixed Integer Linear Programming 
$SA$  Simulates Annealing 
$PSO$  Particle Search Optimisation 
$GA$  Genetics Algorithm 
$PS$  Pattern Search 
$RR$  RoundRobin 
$PLC$  Power Line Carries 
$CC$  Computational Cost 
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Appliance Configuration  

Consumption (kWh)  Fixed consumption (kWh)  Duration (hours)  Time ON  Time OFF 
$v{\mathcal{D}}_{i}^{t}$  $f{\mathcal{D}}_{i}^{t}$  $\mathcal{L}$  ${t}_{beg}$  ${t}_{end}$ 
ID  Appliance  Model  Watts (W)  Efficiency Ranges European Union $\mathit{A},{\mathit{A}}^{+},{\mathit{A}}^{++},{\mathit{A}}^{+++}$  Estimated Average Power in 24 h (kWh)  Estimated Standby Power in 24 h (kWh)  Estimated Operative Time in 24 h (h) 

$AP1$  Water Heater  Wesen ECO30  2000  10–14.73  0.010  1–15  
$AP2$  Clothes Dryer  Balay 3SB285B  4350  1–2.22  0.015  1–10  
$AP3$  Clothes Washer  Eutrotech 1106  1800  1–2.67  0.015  0.5–10  
$AP4$  Iron  Rowenta DX1411  2100  0.1–3  0.002  1–3  
$AP5$  Air conditioner  Fujitsu STG34KMTA  9400    3.9–24.3  0.015  0.3–15 
$AP6$  Room air conditioner  Rinnai RPC26WA  2600    8–24.3  0.015  3–18 
$AP7$  Heater  DeLonghi HSX3324FTS  2400  1–7  0.08  0.1–10  
$AP8$  Fan heater  Dyson AM09  2000    1–6.7  0.015  0.1–10 
$AP9$  Dehumidifier  DeLonghi DEX  210  4–24.3  0.005  1.1–9  
$AP10$  Electric blanket  Medisana HDW  120    1–3  0.08  1.2–9 
$AP11$  Ceiling Fans  Westinghouse Bendan  80  0.5–9  0.01  0.5–5  
$AP12$  Attic Fans  Remigton  500    4.73–6  0.01  0.1–18 
$AP13$  Tower Fan  Sunbeam FA7250  40    1.4–3  0.03  0.1–18 
$AP14$  Hoover  BGLS4TURBO  750    3–6  0.02  0.3–18 
$AP15$  Boiler  Greenstar Ri  9000  8–22  0.05  0.1–3  
$AP16$  Coffee maker  DeLonghi ECOV  1100  9–12  0.05  0.1–3  
$AP17$  Refrigerator  Bosch KDN46VI20  500  8.77–10  0.05  4.77–24  
$AP18$  Dishwasher  Bosch SMS88TI36E  1500  0.5–1.5  0.015  0.3–4  
$AP19$  Food processor  Becken BFP400  110  0.5–2  0.015  0.1–5  
$AP20$  Freezer  Bosch GSN36BI3P  350  6–8  0.009  0.1–24  
$AP21$  Microwave  Balay 3CG5172N0  1700  0.9–3  0.01  0.1–4  
$AP22$  Oven  Bosch VBD5780S0  5000  10.96–12  0.01  0.1–8  
$AP23$  Toaster  Russell Hobbs 21973  1100  0.2–1  0.01  0.1–1  
$AP32$  Lighting  Osram  100    0.7–3  0.01  0.1–24 
$AP25$  Vaporizer  Philips GC362/80  400  0.3–2  0.07  0.1–8  
$AP26$  Printer  HP Officejet 3833  100    0.8–1  0.05  0.1–4 
$AP27$  Computer  Samsung ls24a450  350  0.7–15.3  0.05  0.1–24  
$AP26$  TV  Panasonic TX43E302B  54  0.1–100  0.05  0.1–24  
$AP29$  Kettle  Philips HD4644/00  3000  6–19  0.01  0.1–1  
$AP30$  Security Alarm  Vbestlife  20    0.61  0.02  0.124 
$AP31$  Auto Cook  MUC88B68ES  1200  1–3  0.09  0.1–3  
$AP32$  Air Cleaner  Balay 3BC598GN  150  1.1–6  0.01  0.1–6  
$AP33$  Vacuum Cleaner  Hoover TH31HO01  1000  0.9–3  0.06  0.2–4  
$AP34$  Electric Fryer  DeLonghi F26237  1800    13–16  0.05  0.2–3 
$AP35$  LedTV  LG 49LJ515V  250  1.9–5  0.05  0.2–24  
$AP36$  Electric Store  Dura Heat EUH4000  4000    2.4–4  0.05  0.3–23 
$AP37$  Speaker  Logitech Z120  180  0.3–4  0.01  0.2–20  
$AP38$  Hair Dryer  Rowenta CV3812F0  2100  0.99–4  0.01  0.2–6  
$AP39$  Smart Camera  Yi Home  4    0.99–2  0.01  0.2–24 
$AP40$  Monitor Sensor  iHome  5    0.99–10  0.01  0.1–24 
Factor  Type  Value 

Community Size  High, Low  30, 5 
N. of Appliances  High, Low  1200, 40 
Distribution of Appliances  Same, Different  S, D 
Fixed Demand  High, Low  Not influenced by optimisation 
Variable Demand  High, Low  Up to 18 kWh${}^{6}$, Up to 9 kWh${}^{6}$ 
Consumer Flexibility  High, Low  24 h, $\mathcal{A}$ duration: $\mathcal{L}$ 
Vector of $\mathcal{RW}$  Even, Uneven  10 kWh, [10 kWh–20 kWh] 50% SD 
Community Size $\mathcal{N}$  N. of Appliances $\mathcal{A}$  Distribution of Appliances  Fixed Demand f$\mathcal{D}$ (kWh)  Variable Demand v$\mathcal{D}$ (kWh)  Consumer Flexibility $\mathcal{CF}$  RW Vector per Hour (kWh)  

Case 1  From 5 to 30  From 40 to 1200  S  Up to 0.43  Up to 9  24 h  10 
Case 2  From 5 to 30  From 40 to 1200  S  Up to 0.43  Up to 9  $\mathcal{L}$  10 
Case 3  From 5 to 30  From 40 to 1200  D  Up to 0.43  Up to 9  24 h  10 
Case 4  From 5 to 30  From 40 to 1200  D  Up to 0.43  Up to 9  $\mathcal{L}$  10 
Case 5  From 5 to 30  From 40 to 1200  S  Up to 0.43  Up to 18  24 h  10 
Case 6  From 5 to 30  From 40 to 1200  S  Up to 0.43  Up to 18  $\mathcal{L}$  10 
Case 7  From 5 to 30  From 40 to 1200  D  Up to 0.43  Up to 18  24 h  10 
Case 8  From 5 to 30  From 40 to 1200  D  Up to 0.43  Up to 18  $\mathcal{L}$  10 
Technology  Standard  Data Rate  Frequency Band  Power Consumption  Complexity Transmission Range  Strengths  Application Areas  Encryption/Authentication 

Bluetooth  IEEE802.15.1  24 Mbps (v3.0)  2.4 GHz  Low  10 m typical  Small networks Security, speed Easy access Flexibility  HAN  Challenge response scheme/CRC32 
WiFi  EEE802.11x  11,54 to 300 Mbps outdoor  2.4 GHz 5 GHz  Very high  Up to100 m  Popular in HAN Speed, flexibility  HAN  4Way handshake/ CRC32 
ZWave  802.11  100Kbps  2.4GHz 868.42 MHz (EU)  Low  30 m indoor; 100 m outdoor  No interferences  HAN, NAN  AES128/ 32bit home I.D 
Zigbee  IEEEE802.15.4  256 Kbps  2.4 GHz  Very low  10–100 m  Low cost Low consume Flexible topology  HAN,NAN  ENCMIC128 Encrypted key/ CRC16 
LPWAN  SigFox LoRaWAN NBIoT  0.3 to 50 kbit/s per channel  915 MHz  Low  10 km in rural settings  Low power Low cost  NAN,WAN  Symmetric key cryptography/AES 128b 
6LoWPAN  IEEEE802.15.4  250 Kbps  2.4 GHz  Low  Up to 200 m  Low energy use  HAN, NAN  Symmetric key cryptography/AES 128b 
GSM/GPRS  ETSI GSM EN 301349 EN 301347  14.4 Kbps (GSM) 114 Kbps (GPRS)  935 MHz Europe 1800 MHz  Low  Several Km  Low cost Signal quality  HAN, NAN WAN  64 bit A5/1 encryption/ Session key generation 
WLAN  IEEE 802.11  150 Mbps  2.4 GHz Europe  Low  250m  Robustness  HAN, WAN  WEP, WPA, WPA2/ Open, Shared EAP 
5G  5G Tech Tracker  Up to 20 Gbps  34003800 MHz awarding trial licenses (EU)  Very Low  46 m indoor; 92m outdoor  High speed Low latency  HAN, WAN  Symmetric key encryption/ Mobility management entity 
3G  UMTS  Up to 14.4 Mbps  450,800 MHz 1.9 GHz  Low  Up to 100 m  Fast Data Transfer  HAN,WAN  CDMA2000/ Authentication and Key Agreement 
Hardware  Features  Communication Transceivers  Operating System  Power Consumption  Strengths/Weakness 

Raspberry Pi 3  1.2 GHz Quad Core BCM2837 64bit CPU 1GB  4 USB, WiFi, Bluetooth, optional ZigBee and ZWave  Raspbian Ubuntu Windows 10  1.8 W  Open source platform; Use Python or C++; Cost: 50 
Arduino  32 MHz Micro controller based on ATmega2560 32 kB  WiFi, Bluetooth, ZigBee, GSM  Processingbased  0.2W  Open source platform hardware/software; High flexibility. Cost: 30; Appliances compatibility 
BeagleBone  720 MHz MR CortexA8 processor 512 MB  1 USB port, PLC, Bluetooth, Ethernet  Angstrom Linux  1 W  Open source platform similar to Raspberry; Easy setting up; Cost: 90 
RADXA  ROCK Pi 4 is a Rockchip RK3399 based SBC six core ARM processor, 1GB  WiFi, Bluetooth 5.0, USB Port, GbE LAN  Linux  2.3 W  Open source platform; High flexibility; Cost:50 
Libelium Waspmote  14.7 MHz ATmega1281 28 kB  1USB, 802.15.4/ZigBee LoRaWAN,WiFi PRO GSM/GPRS,4G modules  Linux  2 W  High flexibility; Starter kit:200; ZigBee,WiFi and LoRaWAN support 
Xilinx Spartan FPGA  16 Mb SPI flash memory, 100 MHz  Ethernet, USB port  Linux  2 W  SH, Deep Learning, Autonomous System 
PYNQ  Embedded systems Xilinx Zynq Systems on Chips (SoCs)  Bluetooth, Ethernet, USB port  Linux  2.3 W  IoT hardware development in Python 
Control4Home Automation  Control4Home owners enjoy personalised smart living experiences  Bluetooth, WiFi ZWave and ZigBee  Licensed    Operation with internet connection; Not user installation 
Nexia  Smart home automation system  ZWave  Licensed    No knowledge of installation required/ Only ZWave support; Low compatibility 
LG smart appliance  Control key features on LG smart appliances from your smartphone  WiFi  Licensed    No knowledge of installation required/ Only for LG appliances; Closed source 
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Cruz, C.; Palomar, E.; Bravo, I.; Gardel, A. Towards Sustainable EnergyEfficient Communities Based on a Scheduling Algorithm. Sensors 2019, 19, 3973. https://doi.org/10.3390/s19183973
Cruz C, Palomar E, Bravo I, Gardel A. Towards Sustainable EnergyEfficient Communities Based on a Scheduling Algorithm. Sensors. 2019; 19(18):3973. https://doi.org/10.3390/s19183973
Chicago/Turabian StyleCruz, Carlos, Esther Palomar, Ignacio Bravo, and Alfredo Gardel. 2019. "Towards Sustainable EnergyEfficient Communities Based on a Scheduling Algorithm" Sensors 19, no. 18: 3973. https://doi.org/10.3390/s19183973