Ant Colony Optimization based Energy Management Controller for

Ant Colony Optimization based Energy Management Controller for Smart Grid Sahar Rahim1 , Zafar Iqbal2 , Nusrat Shaheen1 , Zahoor Ali Khan3,4 , Umar Qasim5 , Shahid Ahmed Khan1 , Nadeem Javaid1, * 1

2

COMSATS Institute of Information Technology, Islamabad, 44000, Pakistan UIIT, Pir Mehr Ali Shah Arid Agriculture University, Rawalpind 46000, Pakistan 3 Faculty of Engg., Dalhousie University, Halifax, NS B3J 4R2, Canada 4 CIS Higher Colleges of Technology, Fujairah 4114, United Arab Emirates 5 Cameron Library, University of Alberta, Edmonton, AB, T6G 2J8 Canada ∗ Corresponding author: www.njavaid.com, [email protected]

Abstract—In this paper, we introduce a generic architecture for demand side management (DSM) and use combined model of time of use tariff and inclined block rates. The problem formulation is carried via multiple knapsack and its solution is obtained via ant colony optimization (ACO). Simulation results show that the designed model for energy management achieves our objectives; it is proven as a cost-effective solution to increase sustainability of smart grid. The ACO based energy management controller performs more efficiently than energy management controller without ACO based scheduling in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization. Keywords: Smart Grid; Demand Side Management; Multiple Knapsack Problems; Time Of Use Tariff; Inclined Block Rate; Renewable Energy Sources.

I. I NTRODUCTION RADITIONAL electrical power system is inadequate to meet modern power grid challenges such as reliability, stability, robustness, etc. [1]. Thus, a new infrastructure is needed to smartly meet these challenges and reduce pressure on global environment. In this regard, smart grid (SG) integrates communication technologies, computational abilities, control systems and sensors with existing grid and enables two way flow of information between utility and end users. Main aims of SG are to enhance efficiency, sustainability, capacity and customer engagement [2]. One of the important aspects of SG is demand side management (DSM) which is the best way to maintain balance between demand and supply. Two main functions of DSM are load management and demand response (DR). Load management focuses on the improvement of energy efficiency [3], while DR is a responsive action taken by a customer against dynamic price models [4]. The common objectives of SG are electricity bill reduction, minimization of aggregated power consumption and minimization of both electricity bill and aggregated power. To achieve these objectives, many DSM techniques and algorithms are proposed in the previous years; integer linear programming [5], mixed integer linear programming [6], mixed integer non-linear programming [7], convex

T

programming [8], etc. However, these techniques can not tackle large number of different household appliances having unpredictable, non-linear and complex energy consumption patterns due to randomness in human behavior. Moreover, to attain electricity cost minimization objective, they ignore user comfort level and their electricity pricing model is also not compatible with real scenarios. In this paper, ant colony optimization (ACO) technique is used for DR (in DSM) due to its exceptional characteristics; flexibility for specified constraints, ease of implementation, low computational complexity and low computational time [9]. We first design an energy management controller (EMC) model smart homes using multiple knapsack problem (MKP) and then apply ACO to get feasible solution for designed objective function. To calculate electricity bills, we use time of use (TOU) tariff model with inclined block rate (IBR), so that peak formation is avoided. Effectiveness of the designed EMC model is shown via simulations where unscheduled and ACO based schedules cases are compared in terms of energy consumption pattern, electricity bill, peak to average ratio (PAR), user comfort level, and execution time. The rest of the paper is organized as follows. Section II briefly describes related work. Section III explains motivation for proposed work. Section IV describes system model and section V deals with problem formulation. Simulation results are discussed in section VI. Finally, paper is concluded in section VII by pointing out the future work. II. R ELATED WORK In [15], authors investigate the problem of household appliance scheduling to enhance energy efficiency of electrical grid and provide benefits to end users. They proposed a solution that optimally schedules a set of appliances. To minimize customer electricity bills and maintain energy consumption within a limit, they use day-ahead variable peak pricing model and map their problem by using MKP. By limiting the energy demand within certain capacity, problem of load shedding can be removed. Results show that this model effectively

Fig. 1: SG architecture

reduces utility electricity bills while keeping power consumption within pre-defined limits. Another model of home energy management controller for residential users is proposed in [16]. Objective function is formulated by knapsack problem and dynamic programming approach is used to solve problem and to set consumer preferences for each appliance. These priorities were the value of appliances that are used to schedule the appliance to satisfy their operational time constraints to avoid peak formation and to reduce electricity cost. In [10], authors present an efficient model of DSM that reduces PAR and electricity bills for residential, industrial and commercial users. Scheduling problem is formulated as a minimization problem and then problem is evaluated by using heuristic evolutionary approach. Heuristic algorithms show better results because of their flexible nature that allow the implementation of individual load pattern in order to minimize inconvenience. Proposed model is beneficial for both utilities and customers in a way that PAR reduction causes minimization in the number of peak power plants while incentive based model helps consumer to reduce their electricity bills. Simulation results show that the proposed DSM strategy achieves significant savings, while reducing the peak load demand of the smart grid. In [11], authors discuss an efficient architecture for energy management system by using home area network (HAN) for residential users. They combine real-time pricing (RTP) tariff model with the IBR because when only the RTP is adopted, there is a risk that most of the appliances operate during the hours of lowest electricity price that cause peak formation. To strengthen the stability of electricity system, peak formation must be avoided. To solve these issues in an optimized way, objective function is formulated. As this kind of optimization problem is non-linear, therefore they use GA to optimize their

problem. Simulation results shows that proposed model is very effective to reduce PAR and electricity cost. Another DSM model is proposed in [12] for residential users to reduce PAR and electricity bill minimization. GA is used to get optimal start time of each appliance in each time slot while satisfying its operational constraints. There is a tradeoff between electricity cost and waiting time. When waiting time of an appliance is zero, its electricity cost is increased and vice versa. Combined model of RTP with IBR is used to avoid peak formation. Simulations are carried out for single and multiple users. Results show the effectiveness of proposed DSM model for both single and multiple user scenarios. An efficient heuristic approach is presented in [17] for scheduling of smart appliances in residential area. The proposed algorithm is evaluated by comparing the electricity cost and computational time with an exact algorithm. Variable energy price model is used for scheduling of appliances. Hourly prices for electricity, the operation start times of set of appliances are optimized to reduce cost of energy consumption while satisfying the operational and peak power constraints. Results show that electricity cost obtained by heuristic algorithm is within 5% of the optimal cost of exact algorithm whereas computational time is reduced by exponential factor. III. P ROPOSED SYSTEM MODEL In SG, DSM enables more efficient and reliable grid operations. Its two main functions are energy management and demand side control activities for end users. In residential area, every smart home is equipped with EMCs and smart meters to make stable and reliable bi-directional communication between utilities and customers. All elements, such as electrical appliances, sensors, local generation and energy storage systems (ESSs) give their information to EMC through HAN

and EMC controls scheduling of appliances. After collecting all information, EMC sends it to SG domain through WAN. There are various wireless solutions for communication links between the smart meters and the EMCs such as ZigBee, ZWave, Wi-Fi, or a wired (HomePlug) protocol [1]. Simple architecture of DSM is shown in fig. 2. In residential area

Residential area domain Smart devices

SG domain

HAN

shiftable appliance is characterized by its length of operation which is denoted as τsed and it is pre-defined by end users each day. 3) Elastic appliances: These are also called interruptible appliances because these are fully controllable in terms of both usage time and power consumption profile. For example, air conditioner, refrigerator, water heater, space heater, etc. We represent elastic appliances by Eed and its power consumption is denoted by κ. Each elastic appliance eed ∈ Eed has power rating ρeed , power quantity factor λeed , length of operation τeed , start time αeed and end time βeed . These attributes are set by the consumer.

Distribution Operation

Sensors

WAN EMCs

Market

Distributed RESs

Service provider ESSs Customers

Two way communication One way communication

Fig. 2: Components of DSM based DSM, we consider N smart homes and M smart appliances. In this model, all smart homes have smart metering system and EMC. End users change their energy usage according to incentive based schemes offered by utilities. In each home, consumer inputs different parameters of appliances to appliances scheduler and then appliance manager gives signal to various appliances about their on/off status. For electricity pricing model, TOU tariff is used to calculate electricity bill against the energy consumption cost per day. In order to design the optimization model for home energy management, we have categorized the load according to the characteristic of appliances and life style of end users as discussed in the following section. A. Load categorization We classify appliances into three categories; fixed, shiftable and elastic appliances according to their power consumption pattern and time of use [18]. Detail of all these categories is given as follow: 1) Fixed appliances: These are also called regular appliances because their usage or length of operation can not be modified. For example, lights, fans, clothes iron, microwave oven, toaster, tv, etc. We represent fixed appliances by Fed and its power consumption as ν. 2) Shiftable appliances: These are also called burst load because these are manageable and can be shifted in time without altering their load profile. For example, washing machine, dish washer, clothes dyer, etc. We denote shiftable appliances by Sed and their power consumption by ∆. Each

B. Energy consumption model Let A = { a1 , a2 , a3 , . . . , am } be the set of appliances such that a1 ,a2 ,a3 ,· · · ,am are number of appliances that belong to each category. If t ∈ T = { 1, 2, 3, · · · , 24 } denotes the scheduling horizon, then hourly energy consumption demand of a appliance is given as, E a (t) = { Eta1 + Eta2 + Eta3 + . . . + Eta24 } Eta1 ,

Eta2 ,Eta3 ,

(1)

· · · ,Eta24

where, denotes energy consumption demand of each appliance in the respective time slots. The per day total energy consumption demand for all appliances is calculated as follows, 24  X A  X (2) ET = E(i,t) t=1

a=1

C. Energy price model A number of tariff models are available to define electric energy prices for a day or for short time duration. Among these, TOU tariff model is defined for electricity prices depend on the time of day and are pre-defined in advance. Critical peak pricing (CPP) is a variant of TOU in which price is considerably raised in case of emergency situations (e.g. high demand). RTP based electricity prices can change as often as hourly, reflecting the utility cost of supplying energy to customers at that specific time. In our model, we use TOU with power dependent tariff known as inclined block tariff or IBR. The energy price at time t is an increasing, piecewise, linear function of the total energy demand. As E(t) is the total power consumption of all appliances in a home at each time slot t and it is calculated as, 24   X ν(t) + ∆(t) + κ(t) (3) E(t) = t=1

To calculate electricity bills, energy price for each unit consumed in each time slot is represented by C(t) and according to IBR model, it is designed as,  1  C1 (t) 0 ≤ E(t) ≤ Eth (t) 1 2 C(t) = C2 (t) Eth (4) (t) ≤ E(t) ≤ Eth (t)   2 C3 (t) Eth (t) < E(t) 1 2 where, Eth and Eth are power consumption thresholds and 1 2 3 C ,C and C are costs for these particular cases.

D. Residential users We design our model for three types of users in residential area; passive, semi-active and active users. 1) Passive users: They only consume electrical energy of the grid and does not generate or store electrical energy. They can only shift there load from high peak to low peak and reduce their electricity bills. The set of passive users is represented by P . 2) Semi-active users: They have RESs such as solar panels and wind turbines. They consume energy both from power grid and RES to reduce their electricity bills. The set of semi-active users is represented by S. 3) Active users: They take energy from RES and store it in storage devices such as batteries as well as also take electrical energy from grid to fulfill their need. The set of active users is represented by A. IV. P ROBLEM FORMULATION In this work, main objectives are to reduce consumer cost by optimizing the energy consumption patterns of appliances to maximize the comfort level of end user. Here, we formulate our scheduling problem by using MKP. MKP is a resource allocation problem that consists of “M” resources (capacities) and set of “N” objects [19]. We take “j” number of knapsacks, and map our scheduling problem in MKP as follows: • We consider “j” number of knapsacks as power capacities in each time slot. • Number of appliances as number of objects. • The weight of each object as the energy consumed by appliances in each time slot. Note that it is independent of “t”. • The value of object in a specific time slot is the cost of power consumption of the appliance in that time slot. • The value of binary variable “χ” can be 0 or 1 depending on the state of electrical appliance. The total power consumption for all types of appliances should not exceed maximum power capacity in each hour denoted as γ(t), we introduce constraint which limits the power consumption and depends on load profile and its states. Constraints show that power consumption is predefined, 24  X

 E(t) × χ(t) ≤ γ(t)

(5)

t=1

Here, γ(t) is the power capacity in each hour that is available from grid and χ(t) ∈ [0, 1] denotes the states of appliances. Total power consumption in each hour must be limited to this capacity factor. A. Objective function and its solution via ACO The overall objective function of our scheduling problem is to minimize electricity bill with optimal use of power from grid and to minimize waiting time (to avoid frustration of end users). Additionally, optimal integration of RESs is also a key point to reduce green house gas (GHG) emission. We

formulate our objective function as an optimization function and is modeled as, 24  A    X X min a1 · (Ea (t) × Ca (t)) + a2 ϕa (t) (6) t=1

a=1

where, Ca is the electricity cost in each time slot that must be minimized while keeping waiting time of shiftable appliances minimized. a1 and a2 are weights of two parts of objective function and their values are a1 , a2 ∈ [0, 1] or a1 + a2 = 1. It shows that either a1 or a2 would be 0 or 1. In this work, our major concern is with electricity cost reduction with maximizing comfort level of end users. For this purposed model, we assume waiting time of each shiftable appliance not greater than 5, if operation start time of an appliance is greater than our assumption then utility pays penalty. Algorithm 1 : Improved Algorithm of ACO-EMC 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:

Initialize all parameters (αa ,βa ,τa ,ρa ) For all users n ∈ N do For all appliances a ∈ A do For all time slots t ∈ T do Randomly generate ant population while Maximum number of iterations and min error not reached do For Each individual ant update pheromone refer [21] For Each individual ant evaluate the objective function using (29) if Ea < E 1 then calculate electricity bill using C 1 else {E 1 < Ea < E 2 } calculate electricity bill using C 2 else {Ea > E 2 } calculate electricity bill using C 3 end if if C(t)is high peak hour then calculate ϕa using (28) else start an appliance end if local update pheromone for each ant refer [21] choose best solution so far global update pheromone for each ant refer [21] repeat until iteration end using Θ(t) when electricity bill is high if E(t)is high then Θ(t) energy else E(t) end if end while

ACO is a meta-heuristic optimization approach that is used to solve discrete combinatorial optimization problems. It has unique properties of self-healing, self-protection and selforganization [13]. In literature, ACO is used for DSM in many ways. For-example, authors in [14], investigate congestion management and cost minimization problems. They formulate their focused problem as a non-linear programming problem and electricity bill minimization is achieved using ACO. To our knowledge, ACO implementation in residential area is not done before. In our work, we use ACO to evaluate the designed

optimization function to get optimized schedules for home appliances. Our scheme gives novel idea to implement ACO as optimization tool for DSM in residential area. In [20], linear programming is used to designed the optimization function. Refer to [21], we modified its algorithm for our designed scenario. Algorithm. 3 gives detailed view of ACO based EMC (ACO-EMC) model. ACO is used to evaluate objective function (refer eq. 29) and its constraints (refer eq. 29a to eq. 29i) to get feasible operational time for all appliances. Our proposed model is applicable for single and multiple homes in residential areas. The improved ACO algorithm is shown in algorithm 1.

TABLE IV: ACO parametric list Parameters Ant quantity Pheromone intensity factor Visibility intensity factor Evaporation rate Trail decay factor Stopping criteria Max. iteration

Values 10 2 6 5 0.5 Max. iteration 600

Simulation parameters of ACO-EMC are given in table. IV, respectively. A. Electricity bill reduction

V. S IMULATIONS AND RESULTS To evaluate different performance metrics of EMC, we conduct extensive simulations in MATLAB. We use TOU tariff model of Jemena Electricity Networks (VIC) Ltd [22], [23] for residential area with IBR. For simulations, we design a model for residential area in which each home is equipped with 10 smart appliances and 4 end users. Appliances with their parametric values that are used in simulations are shown in table. I, table. II, and table. III, respectively. In table. I, fixed appliance has only ρa parameter measured in kWh because these are non-manageable appliances and do not play any role in load scheduling problem. Whereas, other two categories

The maximum value of electricity bill in unscheduled model is 266.3492 cent as shown in fig. 3. It is reduced to 114.2536 cent in ACO-EMC. During peak hours (16-22), sufficient electricity cost reduction is shown for the designed ACO-EMC model. ACO-EMC acts more effectively than the unscheduledEMC BPSO-EMC in achieving our designed objective of electricity cost reduction due to its characteristics of local and global exploration.

TABLE I: Parameters of Fixed Appliances ρa (kWh) 0.6 0.75 1.5 1.18 0.5 0.8

Appliances Lighting Fans Clothes iron Microwave oven Toaster Coffee maker

of appliances; shiftable and elastic appliances are known as schedulable appliances. As, in table. II, the parameters for shiftable appliances are αa , βa , ξa , ϕa and ρa are kWh. ϕa is the unique parameter in shiftable appliance because these appliances can be interruptible during its length of use. For elastic appliances, the parameters are αa , βa and ρa in kWh are shown in table. III. TABLE II: Parameters of Shiftable Appliances Appliances Washing machine Dish washer Clothes dyer

αa (hours) 8 7 6

βa (hours) 16 12 18

ϕa (hours) 5 5 5

ρa (kWh) 0.78 3.60 4.40

TABLE III: Parameters of Elastic Appliances Appliances Air conditioner Refrigerator Water heater Space heater

αa (hours) 6 6 6 6

βa (hours) 24 24 24 24

ρa (kWh) 1.44 0.73 4.45 1.50

Fig. 3: Electricity bill (cent) B. PAR Performance of the designed model (ACO-EMC) with respect to PAR reduction is shown in fig. 4. It shows that PAR is significantly reduced for ACO-EMC as compared to the unscheduled case because these are designed to avoid peak formation in any hour of a day. Results prove that our proposed model effectively tackle the peak formation problem. PAR curves for ACO-EMC describe that power consumption of appliances is optimally distributed in 24 hours without creating peak in peak hours (16-22) of a day. We have used combined model of TOU and IBR for electricity billing to avoid peak formation via giving information to consumers. C. Waiting time User comfort is related to both electricity bill and waiting time of an appliance. In order to achieve lower electricity bills, smart users must operate their appliances according to optimal schedule of EMC. During scheduling horizon of

shiftable appliances, operational time is not fixed due to price variation in dynamic pricing models. Generally, it is observed that electricity cost reduction and waiting time show inverse relationship. By applying waiting time constraints on the objective function, we have enhanced the performance of EMC in terms of user comfort and electricity bill reduction. In fig. 5, it is shown that electricity bill is high if rate of waiting time is zero and it is low with increase in rate of waiting time for the proposed model.

Fig. 4: PAR curve

Fig. 5: Possible trade off between electricity cost and waiting time

VI. C ONCLUSION AND F UTURE W ORK In this paper, we have presented an efficient DSM model for residential energy management system in order to avoid peak formations while decreasing the utilities electricity bill by preserving user comfort level within acceptable limits. We used ACO to solve our objective function and used combined pricing models, TOU tariff and IBR model for electricity bill calculation. From the results, it is clearly justified that our proposed model works efficiently in terms of electricity bill reduction, and minimization of PAR while considering user satisfaction. In future, we will focus on human behavior to achieve comfort level of consumer and to minimize frustration cost and improve security and privacy issues between end user and utility.

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