An Efficient scheduling of power and appliances ...

An Efficient scheduling of power and appliances using metaheuristic optimization technique Nasir Ayub1 , Adnan Ishaq1 , Mudabbir Ali,Muhammad Azeem Sarwar1 , Basit Amin and Nadeem Javaid1,∗

Abstract Nowadays, Energy become the most valued necessity. Energy crisis becomes a critical issue of this era. Energy demand is increasing day by day, due to which peak load creation occurs. In order to handle the critical situation of the energy crisis, many techniques and methods are implemented. This can be done by replacing the traditional grid with smart grid and scheduling of appliances at Demand Side Management (DSM). Our main focus is on load management and minimization of cost which can be done by load shifting from on peak hours to off peak hours. We have achieved objectives by using two meta-heuristic optimization techniques; Harmony Search Algorithm (HSA) and EarthWorm optimization Algorithm (EWA). Simulation results show that the approaches we adopted reduce the cost, reduce the Peak Average Ratio (PAR) by load shifting from on peak to off peak hours between the min and max interval with a low difference.

1 Introduction Science has blessed the human life with many valuable technologies. Electricity is one of the most blessing for human. Electricity is generated by nuclear power plants,hydro power plants and wind power plants etc. as shown in fig 1. Electricity is used by industrial and household appliances. As the population increases, the demand of electricity increases. Energy demand is increasing daily with the passage of time [12]. To handle demands, energy provider should stimulate the consumers about the usage of electricity in well-organized manner. The consumption of energy in buildings is much higher than economic sector [13].this sector needs to be made more economical. The consumption of electricity can be reduced to some extent without knowing to the user [14]. This can be done by adapting DSM in aspect of electricity demand 1 COMSATS

Institute of Information Technology, Islamabad 44000, Pakistan www.njavaid.com, [email protected]

∗ Correspondence:

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Fig. 1: Smart Grid

controlling in a proper way. It is allowed by DSM that the user to be informed about their energy usage [15]. An Energy Management Controller (EMC) is defined at demand side which distributes the energy in efficient way. DSM is a feature of SG which is used for management of energy. DSM is used in an area like residential, commercial and targets the peak load reduction. Different operational and energy measures are included in SG are smart meters, smart appliances etc. DSM programs are made for the consumers in order to facilitate them in managing their loads and also provides a balance between supply and demand. The DSM programs encourages the user for shifting their load from high peak hours to the low peak hours. DSM has two main functions; Demand Response (DR) and load management. In load management, the energy is managed in well-manner way. It helps in the reduction of PAR and power consumption. DSM can be explained as deployment, monitoring, and planning strategies of consumer that has an impact on usage and the load [16]. Utility has described pricing schemes for the consumer for the calculation of bill. Dynamic pricing schemes which include Time of Use (TOU), Inclined Block Rate (IBR), and Critical Peak Pricing (CPP), RTP and Day Ahead Price etc. Users are encouraged for shifting their high energy consumption appliances to the low peak hours, which minimizes the electric cost and also reduces PAR. Pricing scheme RTP is considered as more efficient for electricity markets. The main objectives of our research paper are; Minimization of electric cost and PAR reduction. Many techniques are implemented in recent years to achieve these objectives. In [1] and [2], integer linear programming, approximate dynamic programming techniques are used to lower the value of electric cost and minimize the PAR. To overcome the lack of the above techniques different types of optimization techniques; meta heuristic techniques can also be used for energy management in SG. In [4] and [5], author used GA, BPSO and WDO for the reduction of cost and maximizing the UC and peak reduction [8]. A Huge amount of energy is consumed

Energy Optimization in Smart Grid using EWA and HSA

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in residential area. These are the facts that attracts the attention of researchers towards the scheduling of appliances at home. We use two Meta heuristic algorithms HSA and EWA in order to achieve our objective function. Section 2 comprises of recent related work. Section 3 depicts the detail description of proposed model. Simulation findings and results are discussed in Section . Lastly, in Section 4, concluding remarks are presented followed by future directions.

2 Related work In SG, many methods have been demonstrated and implemented by using different algorithms. The main focus in these techniques is to find an optimum scheduler in aspect of giving benefit to utility and consumer. Different types of scheduling algorithms have been used while considering different parameters in mind such as prices schemes, appliances, user demands etc. While considering these parameters in mind, these algorithms have been implemented. In [1], author use Integer linear programming (ILP) to schedule the optimal operation time and optimal power for power shiftable and time shiftable appliances respectively, according to the user desire. The proposed technique ILP for DSM in SG is capable of scheduling operation time and load of appliances according to customer perspective. Increasing joint scheduling the load and power trading is another problem with energy renewable resources. A technique proposed in [2] approximate Dynamic Programming [DP] to plan the operation time of appliances and to handle the complexity of tradeoff between UC and electric cost, a benders decomposition approach is used. In this article, plan the operation time of different kinds of appliances and a method was implemented to model the communication of the users with spare power generation. Usage of more energy has been reduced by vend their spare energy to local consumer at a lower price than utility. A user demands for energy from the utility. Utility provides the energy to consumer but consumer didnot utilize all the provided energy. Moreover, some of the energy become spare. User sells that spare energy to another consumer with a low price than utility. User generates their own energy by the integration of RES. User also sells the spare generated energy to other consumers. Author used RTP for calculating the electric bill. GA is used for the scheduling of home appliances and for decreasing the cost, RTP is used with Inclined Block Rate (IBR) to achieve low cost and PAR [3]. The usefulness of RTP with IBR pricing pattern is very high. The useful features gained by implementing the proposed scheme GA which makes a decrease in the electricity cost as well as delay time of home appliances operations at same time. Reduction in PAR and cost are managed consistently but ignored the user comfort. Five heuristic algorithms GA, Binary Particle Swarm Optimization (BPSO), BFOA, WDO and a hybrid GWD are used for scheduling appliances in [4]. A tariff signal price RTP is used. Using these heuristic algorithms minimization in electricity cost, PAR reduction and UC was achieved. Hybrid GWD performs best in

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minimization of cost than the other heuristic algorithms. GA provide an optimal solution for the scheduling of appliances during off peak hours and on peak hours. The GA beat WDO, BFOA and BPSO in aspect of electricity bill cost and energy consumption. Good results are achieved for both single and multi-homes. To achieve effectiveness in terms of energy consumption, electricity bill, PAR, UC level, execution time, three meta heuristic algorithms are used GA, BPSO, and Ant Colony Optimization (ACO) [5]. GA performs better comparing with other two techniques in aspect of PAR reduction, minimize electric cost, minimize waiting time and optimal integration of RES. Other parameters are achieved but neglects the RES integration cost and power consumption management. [6] Author pointed the unscheduled way of appliances and the maximization of cost in a residential area. Peak load is created in few hours in the day where the demand and consumption of energy is high. To tackle this problem, author used BPSO optimization technique for scheduling of appliances and TOU price signal. It achieves reduction in the PAR by shifting load from high peak to low peak hours. UC are sacrificed and integration of RES is neglected. There is a contradiction between electric cost and UC. In order to reduce the power consumption, it gains reduction in payments with no gain in user comfort. Optimization power scheduling scheme is used for reducing energy consumption and day ahead price including integer and continuous variable. Using the proposed technique, achieved the reduction in electricity bill [7]. A desired tradeoff is also achieved between payment and user comfort. To maximize UC along with the reduction in electricity cost, a heuristic technique WDO is used in [8]. A model is created by categorizing the appliances into three categories based on power usage model and UC. The classification of appliances is grounded at hourly electric price TOU during low peak hours and high peak hours. Another model is proposed in [8] is Knapsack based on Wind Driven Optimization (KWDO). KWDO is used to maximize electric cost saving, which is further used as comparison of performance estimation of energy consumption in HAN. In the identical paper min-max regret based knapsack problem is used to reduce the higher cost and user peak load. Optimization practices are used for arranging appliances. During peak hours, RES is incorporated; in order to achieve grid stability, electric cost reduction and maximum UC. This produced a tradeoff between saving the cost and appliance waiting time, finally effect UC. However, achieving the cost reduction along with UC and also integrating RES, installation cost of RES is not defined, which directly effect on the total cost. Energy gain from Grid or RES, when not used is wasted. An innovative power structure along with bidirectional information movement among service provider and users is employed to achieve balance load and diminish demand supply mismatch. Renewable energy created by consumers which can be vended to further consumers and also to the grid. In this regard, a novel Prosumer based Energy Sharing and Management (PESM) scheme for corporative DSM is presented in [9]. Prosumer; which consume energy as well as vend/share more energy produced by RES with grid or further consumers at community. The PESM scheme incorporate with prosumers that are connected to grid and generation of

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energy through RES. The scheme minimizes demand supply miss-match and give priority to users; which have shortage of energy at any time. Priority of users is based on different criteria; user who have greater capacity having higher the priority and user who have lower capacity having the low priority. Users having shortage and highest installed renewable capacity will be served first. Usage of energy become efficient by the sharing of energy. As Prosumer can share energy only to grid or within same community [9]. The limitation is that it cannot share energy to another community. In [10], a system is proposed to develop a generic DSM model for domestic users to achieve low value of PAR, total energy cost, and appliances waiting time along with fast performance algorithm. GA based method is used for scheduling appliances in smart grid situation. Scheduling the appliances by GA technique are taken from utility accordingly to RTP values. The consumption of power in particular time slot can be managed by using the shifting load strategies as a replacement for the reduction in load. Those appliances are scheduled only which can be delayed. The execution stops when the achieving of best solution is confirmed. Appliances that are the most critical for UC and cannot tolerate delay are considered as NDAs. Many of NDAs have small power consumption and little influence on the total power consumption of a user if it is kept in some described range. The GA technique is best to schedule every appliance for Energy Management Controller Unit (EMCU) will select a suitable beginning time of every appliance and regarding the RTP and power capacity limitations.it causes a decrease in PAR and saving electric cost.

3 Problem Statement In recent years, the problems of energy management in SG are tackled by different optimization techniques. It is difficult to handle the schedule of energy consumption, minimum cost and maximum the User Comfort (UC) in SG. . Mostly PAR and UC is neglected. In SG,using a smart meter creates a two-way communication between the utility and consumer . smart homes uses smart meter to manage its energy consumption. In SG, energy optimization problems are: • • • •

Minimize the electric cost Minimize energy consumption Minimize the creation peak load Minimize waiting time of appliances to achieve user comfort

In some hours of the day, when the consumption of energy is comparatively higher than the demand. It creates a peak in load, at that hours the price of consumption of electricity is comparatively high. which are on peak hours. If the same situation of peak load creation is happened at off peak hours, it changes into on peak hours by utility [7]. To avoid peak load creation, we have proposed an optimization algorithm which schedules the appliances in such a way that energy consumption is carried out efficiently.

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Fig. 2: Smart residential DSM Components

4 System Model In SG architecture, DSM facilitates more proficient and reliable user tasks. The main functions of SG are energy controlling with demand side switch activities for users. In residential area, every home is equipped with smart meters. Demand side activates for the end users are controlled by DSM. It educate the consumer to consume the most of its energy at off peak hours. To solve the optimization problem, several of optimization techniques are used. In our proposed model, we have categorized 6 appliances into 3 classifications; interruptible, non-interruptible and fixed appliances. The categorization of appliances in our paper is according to [7]. Classified appliances are mentioned below

Table 1: Parameters of appliances Appliance

Power (kWh)

LOT (hours)

Washing Machine Air conditioner Refrigerator Lighting Toaster Kettle

0.78 1.44 0.50 0.6 0.5 0.8

5 6 24 8 2 1

In addition to this we have used TOU as pricing scheme for calculation of bill. The main objective of all this study are: minimize the consumption of energy in order to reduce the electricity cost and reduction in PAR. The main focus is on the minimization of total cost calculated according to the equation 1 with PAR reduc-

Energy Optimization in Smart Grid using EWA and HSA

tion. 24

Cost = ∑

7

! App hour ERate ∗ PRate

(1)

t=1

App Load = PRate ∗ App

(2)

max(Loads ) Avg(Loads )

(3)

PAR =

For optimizing the electricity consumption in HEMS we have used bio-inspired meta-heuristic algorithm; earthworm optimization technique [11]. For that we have initialized the population size as 30 and initialized the iteration as maximum generation index of 50. In EWA, there are two kinds of reproduction; reproduction 1 and reproduction 2. In EWA, reproduction 1 generates only 1 offspring either from male or female while in reproduction 2, generates one or more than 1 offspring at one time. In between them crossover operators are used in order to improve the version of crossover then do mutation is implemented to extract the best value after the iterations. Algorithm of EWA is given below as described in [11]. Next is to compare EWA with BFA. We have merged both technique for same appliances classification.In order to define the solution for better exploration , we have shown the performance of unscheduled appliances as well as scheduled appliances.

4.1 Optimization Techniques 4.1.1 EWA The reproduction conduct of earthworms state multiple optimization problems, the reproduction steps of earthworms can be perfect by the following guidelines. • Every earthworm have the ability of producing off springs and each earthworm individual have two types of reproduction. • Every child of earthworm singular generated holds all the genetic factor whose length is equivalent to parental earthworm. • The earthworm singular with the finest fitness permit on straight next generation, and cannot be altered by operators. This can be an assurance that population of earthworm cannot fail in the increment in generations.

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Algorithm 1 EWA for SG scheduling 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21: 22: 23: 24: 25: 26: 27: 28: 29: 30: 31:

Step 1: Start Initialization. At first Set t = 1 which is generation counter Set the counter of generation t=1 Set population as P of NP earthworm Select the individual in search space randomly Set the numbers of earthworms kept nKEW, maximum generation MaxGn, as similarity factor, proportional aspect , constant = 0.9. Step 2: evaluation of Fitness. Set every earthworm aspect to its position Step 3: while till best solution is not achieved or t < MaxGen do sorting all earthworms according to the fitness value for i = 1 to NP (all earthworm) do Generate offspring xi1 through Reproduction 1 Generate offspring through Reproduction 2 end for Do crossover if i > nKEW then set the number of particular parents (N) and the produced off springs (M) Select the N parents using method i.e. roulette wheel selection; Generate the M off springs ; Calculating xi2 according to offsprings M generated else Randomly an indivisual earthworm as xi2 Update the location of earthworm end if for j = nKEW + 1 to NP (earthworm individuals non-kept ) do do Cauchy mutation end for Calculate the population according to the newly restructured positions; t = t + 1. end while Step 5: show Output of the best solution. End.

4.1.2 HSA It an evolutionary algorithm that have musicians behaviour. Main steps that are involved in HSA are: memory based play ,random play and pitch adjustment.

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Algorithm 2 HSA for SG scheduling 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21: 22: 23: 24: 25: 26:

Step 1: Parameter and problem initialization Harmony memory size (HMS) Harmony memory considering rate (HMCR) Pitch adjusting rate (PAR) Stopping criteria Step 2: HM initialization Initialize the population randomly Step 3: create a new harmony Memory consideration Choosing any value from HM Value of HMCR specify the probability of selecting value from previous values that are stored in HM Pitch adjustment Every component of the New Harmony chosen from HM, is likely to be pitch-adjusted. Similar to Mutation procedure in genetic algorithm Random selection Take a possible range and select random values Increase the diversity of the solutions Step 4: Update the Harmony memory if new values of HM are better than previous worst harmony, then replace the previous worst harmony with new one in the HM then if xnew < xworst then Update the HM as xworst = xnew end if end if Step 5: Termination If the given criteria is satisfied, selection and calculation terminated. Otherwise, repeat step 3 to 4

5 Simulations To assess the performance of our proposed optimization techniques, we have carried out extensive simulations in MATLAB. In these experiments/simulations, we compare our objectives I.e. PAR reduction, pattern of energy consumption, electricity bill reduction and UC.

Table 2: HSA Parametric List Parameters Values Appliances 6 Reproduction 5 Max iteration 100 Population 50

In figure 3, the consumption of energy of electrical appliances in the EWA algorithms is low during the (0 to 3) hours, while with HSA algorithm appliances consume more energy. During high peak hours (7 to 9) h, In comparison to HSA al-

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Table 3: EWA Parametric List Parameters Values Max Itr 50 Population 30

gorithm ,energy consumption of appliances is very low in EWA algorithm . During (10 to 24) h, the normal consumption of energy of both the unscheduled and scheduled cases is the same. Most of the appliances are scheduled by EWA algorithm for a low electricity bill. While the electric prices are very high during (6 to 9) h and (11 to 13) h and maximum number of appliances in these time slots are scheduled by HSA algorithm .On other hand, the EWA algorithm uses the low peak time slots and donot turn on any appliances during the high peak hours and also complete the working cycles. We come to conclude that the EWA algorithm reduce the energy consumption efficiently by scheduling the appliances in mid and low peaks hours.

5 Unscheduled EWA Scheduled HSA Schdeduled

Load (kWh)

4 3 2 1 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

Time (hours) Fig. 3: Load with CPP

Figure 4 elaborates the comparison of HSA and EWA algorithms in terms of electric cost. It is clear from figure 4 that Energy Management Controller (EMC) schedules the appliances in low time slots to minimize the energy consumption. During (0 to 6) h, the electricity costs that is scheduled by EWA and HSA algorithms are relatively same, because the scheduling of appliances by EMC is according to the low pricing slots without taking into consideration of the maximum capacity of appliances limit. During high price hours (7 to 9) h, the electric cost of HSA algorithm is higher than the EWA algorithm,It is because greater number of appliances are scheduled by EMC in these time slots. During the shoulder peak hours (10 to 13), HSA turn on a greater number of appliances in comparison with EWA, so the electric bill cost is higher. During the remaining hours (15 to 24) h, the working times of all the appliances are completed. It is clear that most of the electric bill is

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low during these time slots. In Figure 5, we come to conclude that EWA algorithm performs better in terms of electric cost than the HSA algorithm.

Electricity Cost (cent)

60 Unscheduled EWA Scheduled HSA Scheduled

50 40 30 20 10 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

Time (hours) Fig. 4: Cost

350 300

Total Cost

250 200 150 100 50 0 Unscheduled

EWA

HSA

Fig. 5: Total cost

At the start of our discussion, we resulting the reduction of PAR in the residential load when we use our proposed energy optimization algorithm. Consumer wants to minimize their total electric bill, while the utility is attentive to provide balanced energy supply. Figure 6, it is clearly shown by our proposed algorithm that it is very helpful in PAR reduction and balancing the consumption of energy. It is also clear

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from the figure 6 that EWA algorithm shows good performance in the PAR reduction than HSA algorithm. As EWA algorithm reduce the PAR by 6.8 % while HSA algorithm reduce the PAR by 9%. It is because avoiding the creation of peak load congestion by our proposed algorithm ,it optimally schedule the home appliances in that hours having low price. To achieve reduction in electricity bill, smart user will must follow the scheduling strategy of EMC to operate all appliances. According to scheduling perspective, starting time of any appliance cannot be fixed because variation of price at every hour. Therefore, the scheduling algorithm that we proposed adjusts the initial time of those appliances which are considered as maximum cost saving perceptive. However, this mechanism save the electric cost bill but can ultimately disturbs the life style of the user. Alternatively, scheduling appliances algorithms can also be made to maximize the UC but it will increase the electric cost bill. There is a contradiction between the two objectives which is hard to achieve them instantaneously. The HSA algorithm is designed for those consumers who have no objection on electricity cost and cannot bear compromise on comfort. While the EWA algorithm is considered as for those consumers who are sensitive about electric bill and can on compromise UC. It shows that there is a contradiction between price and waiting time. If price increases the UC decreases and vice versa. Therefore, we designed a scheduling algorithms to minimize the appliances waiting time and electric cost. Figure 7 shows that EWA has more waiting time in appliances than the HSA.

20

Peak Average Ratio

PAR

15

10

5

0 Unscheduled

EWA

Fig. 6: Peak to Average Ratio

HSA

Energy Optimization in Smart Grid using EWA and HSA

Waiting time

4

Waiting time

13

3

2

1

0 EWA

HSA Fig. 7: Waiting Time

6 Conclusion In this paper, we have proposed a heuristic optimization techniques for the scheduling of appliances at residential side to avoid peak creations while focusing on the electricity bill reduction by preserving user comfort level to an acceptable limits. We evaluate our designed objective functions using two heuristic algorithms (EWA and HSA) and analysis the comparison for all of them. Our proposed model used TOU as pricing scheme for bill calculation. It is clearly justified that the model we proposed works efficiently with EWA than HSA in terms of PAR reduction, electric cost minimization while considering the UC. In future, we will focus to achieve the user comfort level and to reduce the frustration cost. We will also work on the different optimization techniques in order to achieve more accurate data in less execution of time.

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