Hybrid Bacterial Foraging Tabu Search Energy ...

Hybrid Bacterial Foraging Tabu Search Energy Optimization Technique in Smart Homes Muhammad Usman Khalid1 , Nadeem Javaid1,∗ , Muhammad Nadeem Iqbal2 , Aqib Jamil1 , Naveed Anwar3 , and Qazi Muhammad Fazal E Haq2

Abstract With the advent of the smart grid, it has become possible to improve the energy systems. To optimize the energy consumption pattern of the appliances, home energy management system is proposed for smart homes. Energy management in smart homes is a challenging task, therefore, the concept of demand-side management was introduced. For the effective scheduling of smart appliance, we propose a metaheuristic optimization technique. The proposed technique is hybrid of two existing techniques: Tabu Search (TS) and Bacterial Foraging Algorithm (BFA). The aim of the proposed technique is to reduce energy consumption so that user electricity bill reduces. Also, improves user comfort in term of average waiting time. For electricity bill calculation and appliance scheduling, time of use price tariff is used. Simulation results demonstrate that proposed scheme outperformed existing schemes in cost reduction and the average waiting time minimization.However, TS outruns other scheduling schemes in peak to average ratio reduction. Key words: Smart Grid, Demand Side Management, Time of Use, Tabu Search, Bacterial Foraging Algorithm

1 Introduction Electricity is the essential need of human beings. Since the mid-20th century, the electricity demand increases exponentially with the increase in human population. The drawbacks of the Conventional Grid (CG) become noticeable such as difficult operation and high cost due to an immense increase in electricity demand. CG is no longer viable to meet electricity demand. Utilities need spinning reserves to meet 1

COMSATS Institute of Information Technology, Islamabad 44000, Pakistan COMSATS Institute of Information Technology, Wah Cantt 47040, Pakistan 3 University of Wah, Wah Cantt 47040, Pakistan ∗ Corresponding Author: [email protected] 2

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the electricity demand that requires a huge budget. Increasing demand for electricity causes an imbalance between supply and demand. This is the main reason behind load shedding also it effects grid stability. The imbalance between supply and demand and irregular usage of electricity lead the energy system towards an energy crisis. The main reason for energy crisis is lack of monitoring and controlling of electricity supply and demand. Scientists start looking for available possible solutions to meet the increasing energy demand. The traditional grid works fine for many years however, this increasing demand for electricity can only be balanced by updating traditional grid and creating smart ones. The concept of Smart Grid (SG) was introduced to overcome the problem of the traditional grid. SG is the implementation of bidirectional communication between the traditional grid and user end. SG effectively utilize the energy resources to ensure the utility grid operation and balance the energy between supply and demand. SG can monitor and control the power consumption in grids as well as in homes. For energy optimization, the utility sends the price signal to the Smart Meter (SM) and in return, SM shares the end user information with the utility. For the energy optimization of smart homes, this information is used. By energy optimization, stress on utility grid decreases and avoids severe blackouts [1]. Demand Side Management (DSM) is the imperative part of SG. The common goals of DSM are energy consumption minimization, Peak to Average Ratio (PAR) reduction, electricity cost minimization, and improving User Comfort (UC). Variety of strategies are proposed by DSM, for example, load shifting, peak clipping, strategic conservation, valley filling and energy efficiency for the load management through Demand Response (DR) [2]. DR motivates consumers to schedule their appliances so that they get profit by minimizing electricity bill. DR programs schedule the appliances according to the price signal provided [3]. Appliance scheduling not only reduces user electricity bill, also improves grid stability. These strategies are beneficial for both consumers as well as utility company. Residential load consumes a large amount of energy approximately 45%. In every home, there is various kind of appliances having different power ratings and working cycles. A sole house is considered in this research work having multiple appliances. The aim is to optimize the home load to achieve overall minimum cost and improve the UC in term of average waiting time. To check the effectiveness of the proposed system, we used two meta-heuristic optimization algorithms: Tabu Search (TS) and Bacterial Foraging Algorithm (BFA). A hybrid has also proposed a merger of BFA and TS. The proposed algorithm is known as Hybrid Bacterial Tabu Search (HBT) algorithm. The adopted and proposed techniques are evaluated using Time of Use (TOU) pricing signal. Results prove that HBT outruns TS and BFA in terms of cost reduction and minimizing average waiting time. However, a compromise exists between UC and PAR. The remainder of the paper is sorted as: a bibliographic review is described in Section 2. reflects the related work. Section 3 illustrates proposed system model. Optimization techniques are presented in Section 4. Section 5 shows the performance evaluation results. The conclusion of the research with future work is discussed in section 6.

Hybrid Energy Optimization in Smart Homes

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2 Related Work There are various studies on DSM. DSM procedures have been broadly utilized in SG contexts such as load scheduling in smart homes, minimizing electricity bill and maximizing UC. In recent years, various optimization techniques are proposed to optimize the electricity load. The goals of these optimization techniques are to modify the pattern of energy consumption so that electricity cost reduces. Also, PAR and waiting time of the appliances keeping in mind. In paper [4], authors proposed multi-objective Mixed Integer Nonlinear Programming (MINLP) for home appliances scheduling. The main objectives are total operational cost minimization, user convenience and thermal comfort level. However, authors do not consider user preferences or pricing scheme influence. In [5], authors are using used Mixed Integer Programming (MIP) with Game Theory (GT) for the scheduling of home appliances. The Renewable Energy Source (RES) is also integrated with scheduling of load which further reduces the stress on CG. The scheme reduces the energy consumption and energy cost. In paper [6], authors presented Home Energy Management System (HEMS) considering Energy Storage System (ESS), Distributed Energy Generation (DG) while using MILP for scheduling purpose. The aim is to minimize cost using dynamic pricing signal and load shaping strategies. Authors consider PAR and cost minimization; however, authors completely ignored UC level. In [7], authors solve residential load scheduling problem by dividing appliances into a number of classes. The load scheduling problem is formulated as MINLP, which is very complex to solve. This issue can be resolved by using Generalized Benders Decomposition (GBD) approach which has low computational time. Each residential appliance scheduled according to their energy consumption pattern. In [8], authors develop a mechanism combining optimization technique, machine learning and data structure to build DR. Loads are divided into three types: fixed, regulate-able and deferrable loads. This mechanism is proposed for the energy management of three type of residential loads. Results show that HEMS using learning based DR is more efficient than all other DR algorithms that authors tested. Authors in [9] proposed an enhanced version of Glowworm Swarm Particles Optimization Algorithm (GSPOA) to make it multi-objective formulation algorithm. The scheduling results of shift-able appliances are compared with the enhanced version of Genetic Algorithm (GA) known as NSGA-2. However, authors do not consider non-shiftable or base appliances. In [10], authors discussed cost efficient residential load scheduling using Fractional Programming (FP) approach. They also considered two pricing signals: real-time and day ahead pricing signal. The aim is to maximize consumption payoff; however, authors do not consider PAR and waiting time of the appliances. In [11], an autonomous HEMS is proposed which consist of both schedulable and real-time appliances. These appliances are connected to the smart meter through HEMS. Authors formulated the problem as MIP problem. They use Dijkstra Algorithm (DA) to solve this problem and proposed low complexity algorithm that performs better than non-optimized scheme. The proposed algorithm has low com-

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plexity and better results than existing approaches. Authors in [12] proposed HEMS with the photovoltaic source, battery storage and DC demand also considering a different type of load profiles. Energy losses between AC and DC systems are minimized by proposed HEMS. Results show that electricity bill is reduced and users with large PV installation and battery capacity receives more energy saving. In [13], authors focus on consumers’ electricity bill. The primary objective is to reduce monthly electricity bill. Authors consider two scenarios a single home and four homes equipped with smart appliances. Appliances are scheduled using TOU pricing signal and Incline Block Rate (IBR). Also, these pricing signals are used for electricity bill calculation. The proposed HEMS converges the energy system towards the target. In [14], two-level HEMS is proposed. One is Local HEMS (LHEMS) and other is Global HEMS (GHEMS). LHEMS schedule the home appliances to minimize the energy consumption in each home. In contrast, GHEMS coordinates the operation of ESS and power trading between neighbouring houses. This improves UC and decreases computational time significantly. In [15] and [16] authors proposed a HEMS to reduce electricity cost and waiting time. In [15], the optimation techniques effectively reduce the cost and PAR; however, a compromise between cost and UC exists. In [16], authors proposed a hybrid scheme of BFA and GA considering RTP pricing signal. Hybrid scheme outperforms both existing techniques in cost reduction and minimizing the average waiting time of the appliances; however, a compromise between electricity cost and PAR exists. The HEMS reduces electricity cost and waiting time of the appliances without using any storage system or RES.

3 System Model The proposed system model is depicted in Fig. 1. A HEMS is proposed to schedule smart appliances to reduce power cost, PAR and average waiting time of the appliances. Smart homes consist of SM, Energy Management Controller (EMC) and smart appliances. SM receives price signal from the utility and sends it to the EMC. In addition, SM receives appliance consumption pattern from EMC and forward it to the utility. Smart appliances send their energy consumption pattern to the EMC and schedule themselves according to the price signal provided by the utility company. For scheduling utility companies, SM, EMC and appliances share their information with each other. A solitary home is considered for scheduling having nine smart appliances. These devices are scheduled in 24-hour time horizon. TOU price signal used for electricity cost calculation. For scheduling, smart appliances are categorized into 3 sets depending upon their consumption and behaviour.


GENERATION

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TRANSMISSION

DISTRIBUTION

EMC

POWER CONSUPTION POWER PRICE TARIFF SMART METER

SMART APPLICANCES

Fig. 1: System Model

3.1 Load Categorization Appliances have different power ratings and energy consumption pattern. Each category depending upon the power ratings and consumption pattern are explained as follows:

3.1.1 Deferrable Appliances Deferrable appliances are those that can be suspended and moved to other time slots by interrupting their operation. They are also known as interruptible appliances. Deferrable appliances in this paper include water pump, vacuum cleaner, dishwasher and water heater. The group of deferrable appliances is represented by 0 D0 0 ∀d ∈ D0 and power consumption is denoted by 0 χd0 . The power consumption in one day of all deferrable appliances is calculated as T

χd = ∑

t=1

∑ Pd × σd (t) dεD

The total cost of all deferrable appliances in time interval t is calculated by

(1)

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Muhammad Usman Khalid et al. T

ςd = ∑

t=1

∑ Pd × δ (t) × σd (t)

(2)

dεD

where T is the total time slots. Pd is the power rating of the deferrable appliances. Electric price is denoted by δ (t) which in this case is TOU and σd (t) is appliance status in a particular time slot t. ( 0, if appliance is OFF. (3) σd (t) = 1, if appliance is ON.

3.1.2 Non-deferrable Appliances Non-deferrable appliances cannot be interrupted or shifted once their operation starts; however, after task completion, they are interrupted and moved to other time slots. The non-deferrable appliances we considered for scheduling purpose are washing machine and cloth dryer. Cloth dryer is immediately scheduled after washing machine operation completed. Non-deferrable appliances denoted by 0 ND0 0 ∀nd ∈ ND0 having power consumption 0 χ 0 . Power consumption for one day of nd non-deferrable appliances is calculated as T

χnd = ∑

t=1

∑

Pnd × σnd (t)

(4)

ndεND

The total cost of all non-deferrable appliances in time interval t is calculated by T

ςnd = ∑

t=1

∑

Pnd × δ (t) × σnd (t)

(5)

ndεND

where Pnd is the power rating of the non-deferrable appliances and T is the total time slots. Electric price is denoted by δ (t) which in this case is TOU and σnd (t) is appliance status in a particular time slot t. ( 0, if appliance is OFF. σnd (t) = (6) 1, if appliance is ON.

3.1.3 Base Appliances Appliances that are not manageable and cannot be obstructed because of their continuous operation are known as base appliances. Total operational time and pattern of electricity consumption of these appliances cannot be altered. User can turn ON these appliances any time. For example refrigerator, oven and AC. Base appliances are denoted by 0 B0 0 ∀b ∈ B0 having power consumption 0 χb0 . The power consumption of all base appliances in one day is calculated as


T

χb = ∑

t=1

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P × σ (t) ∑ b b

(7)

bεB

The total cost of all base appliances in time interval t is calculated by T

ςb = ∑

t=1

P × δ (t) × σ (t) b b ∑

(8)

bεB

where T is the total time slots. Pb is the power rating of the base appliances. Electric price is denoted by δ (t) which in this case is TOU and σb (t) is appliance status in a particular time slot t. ( 0, if appliance is OFF. (9) σb (t) = 1, if appliance is ON. The total energy consumed and total cost per day is calculated by χT = χd + χnd + χb

(10)

ςT = ςd + ςnd + ςb

(11)

Appliances according to their group, power rating and daily usage are listed in Table 1. Appliance Class Interruptible appliances

Appliances Vacuum cleaner Water heater Water pump Dish washer Non-interruptible Washing machine appliances Cloth dryer Base appliances Refrigerator AC Oven

Power Rating (kWh) 0.7 5 1 1.8 0.7 5 0.225 1.5 2.15

Daily Usage (hours) 6 12 8 8 5 4 18 15 10

Table 1: Appliances Parameters

4 Optimization Techniques 4.1 BFA Passino introduced BFA which is a meta-heuristic technique [14]. BFA working architecture is based on bacteria foraging strategies. The algorithm starts with the

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random generation of population sized N p , and each entity is considered as a bacterium. Equation is used to calculate the fitness of each entity Fx . Algorithm steps are discussed below: 1. Chemotaxis step: Bacteria fitness Fx is calculated by adjacency to other bacteria new position φx after moving across the surface. Tumble direction ranges from -1 to 1 and step size S. Random direction is represented by 4x . 2. Reproduction step: Cells those performs well take part in the next generation. 3. Elimination and dispersal step: Cell that doesn’t perform well are discarded and a new random samples are inserted. After all, the best one selected for scheduling. M

Fx [m, n, o] =

∑ (100 × (φ (x, e + 1) − (φ (x, e))2 )2 + (φ (x, e) − 1)2 ).

(12)

e=1

Where φx is calculated using equation 13. 4x φx [m, n, o] = φx [m − 1, n, o] + S p 4τx 4x

(13)

4.2 TS Fred W. Glover introduced TS. TS used for solving combinational optimization problems. TS uses neighbourhood solutions to move iteratively from one particular solution to an improved solution in the neighbourhood until some stop criteria have been satisfied. In order to avoid being stuck in local search, TS carefully explores the neighbourhood of each solution in search space. The forbidden steps submitted in the memory structure known as tabu list (TL). TS steps are given as follows: par 1. 2. 3. 4. 5. 6. 7. 8.

Creates neighbourhood solutions from the current solution. Create a TL that records prohibited actions. Set Aspiration Criteria (AC). Perform TS. Find a best neighbouring solution. If the neighbour is an accessible move to it and replaces the current solution. If not, select the next best neighbour. Check stopping criteria to terminate

4.3 HBT Hybrid is the combination of two or more techniques. Here we adopted HBT algorithm. In BFA, there are three stages: chemotaxis step, reproduction step and elimination, and dispersal step. All steps of the BFA will run in the same way as


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described above; however, we utilize TS to choose the best generation of all generations. Hybrid technology is more successful than existing strategies since it has the best kind of both existing systems. The proposed approach is effective in cost and average waiting time reduction.

5 Simulations and Discussions This section describes the implementation and results of the proposed technology, validated by simulation in MATLAB. To verify the results of the proposed technique compared to BFA and TS. A solitary home is considered having nine appliances that should be scheduled. These appliances are sorted into three types: set A, set B and set C. Set A contains the interruptible devices which incorporate vacuum cleaner, water heater, water pump and dish washer. Set B has non-stop devices that include washer and dryer. You can not stop these appliances during the work cycle. In our simulation scenario, the washing machine is always scheduled for using the washing machine. Set C includes non-intrusive basic appliances such as refrigerators, AC and ovens. The implementation of the proposed technology is evaluated using the TOU price tariff rate. In TOU, prices are divided into multiple blocks of the day, and the price of each block is set to a fixed non-peak time and peak time. The electrical load around 24 hours shown in the figure 2. The power utilization of scheduling technology is less than that of non-scheduling technology. By utilizing scheduling systems, more load has been moved from on-crest hours to off-crest hours to lessen power cost and PAR. Fig. 2 demonstrate that the maximum energy consumption for a day in the unscheduled situation is 13.525 kWh and for TS, BFA and HBT is 9.98 kWh, 9.685 kWh and 10.405 kWh, respectively. For scheduling, the total power consumption is relatively low. Fig. 3 shows the comparison of the electricity cost per hour for an unscheduled TS, BFA alongside proposed approach HBT appears. The results clearly show that the cost during peak hours is lower when compared to unscheduled loads because the scheduling technique moves the load from peak time to off-peak time. The proposed hybrid approach achieved the goal of cost savings compared to TS and BFA. In Fig. 4 shows the difference in total electrical costs between TS, BFA, and HBT, as well as unscheduled. In this figure, it is clear that HBT achieves minimum cost compared to unscheduled, TS and BFA. In unscheduled case, the total cost is 1911.4 cents and decreases to 1539.4, 1601.2 and 1232.8 cents for TS, BFA and HBT respectively. Costs are reduced in all scheduling schemes; however, BFA is the most costly of all scheduling schemes. Fig. 5 demonstrates that load before and after scheduling the appliance remains same because we are scheduling the load from one point to other not shedding the load. Fig. 6 describes the comparison of the PARs achieved between the unscheduled, TS, BFA and HBT. PAR is the maximum peak formation for 24 hours. PAR reduction is not only beneficial to utility users, it also reduces the cost and load to the user and improves grid stability for the utility. If unscheduled case PAR is 5.2476

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Load(kWh)

10 8 6 4

UnScheduled TS Scheduled BFA Scheduled HBT Scheduled

200 Cost(cents/kWh)

UnScheduled TS Scheduled BFA Scheduled HBT Scheduled

150

100

50 2 0

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time (hours)

Time (hours)

Fig. 2: Per hour scheduled load

Fig. 3: Electricity cost with detail price signals

150

1500 100 load

Total Cost (cents$)

2000

1000

50 500

0

Unschedule

TS Schedule

BFA Schedule

HBT Schedule

Fig. 4: Total electricity cost for a day

0

Unscheduled

TS

BFA

HBT

Fig. 5: Load before and after scheduling

and TS, BFA and HBT reduce PAR to 2.5559, 2.6577 and 3.1122, respectively. PAR diminish in all scheduling schemes; however, maximum PAR reduction is achieved by TS. viably diminishes PAR by similarly appropriation of load keeping away from the top formation. Fig. 7 demonstrates the user convenience in terms of average waiting time. Average waiting time is the amount of time that you have to wait for the appliance to turn on. Users always prefer low power prices to reduce electricity bill. TOU signal has a minimum PAR of 2.55 for TS and that is 51.33% less than the unscheduled PAR. Whereas, TS saved only 19.72% of electricity bill and has a maximum waiting time of 4.92 hours. BFA’s average waiting time is relatively less than TS, but BFA has the least cost savings in all scheduling schemes. The HBT has a minimum wait time compared to all other scheduling schemes. HBT reduces costs by 35.53%, HBT has better performance compared to other scheduling schemes; however, HBT has maximum PAR over all other methods. This shows that there is a tradeoff between UC and PAR.


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Waiting Time(hours)

5

PAR

4 3 2

8

6

4

2

1 0

Average TS Average BFA Average HBT

Un Scheduled

TS Scheduled

BFA Scheduled

HBT Scheduled

Fig. 6: PAR for all adopted and proposed schemes

0

Interruptible Load

UnInterruptible Load

Base Load

Fig. 7: Average waiting time of appliances

6 Conclusion and Future Work In this section, the paper is concluded. We proposed a HEMS for load management in smart homes to reduce the user’s electricity bill. A hybrid metaheuristic optimization technique is proposed in this paper using two existing techniques: TS and BFA. Home load management is executed using existing and proposed optimization techniques. To check the validness of the proposed hybrid scheme, we compared the simulation results with existing optimization techniques. Optimization techniques are effective in scheduling the load towards low price interval from high price intervals depending upon price signal provided by the utility. By moving the load to off-peak hours user electricity bill reduces; however, sometimes PAR increases due to maximum load is scheduled at off-peak intervals. A solitary home is considered for simulation purpose. The home consists of nine appliances which are categorized on the basis of their power rating and operational time. Simulation results validated that our proposed hybrid optimization technique is efficient than existing techniques in cost reduction and minimizing the average waiting time of the appliances. A compromise exists between PAR and UC. This research can be extended using multiple homes and different price signals.

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