Exploiting heuristic algorithms to efficiently utilize energy management ...

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Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources Sahar Rahim1 , Nadeem Javaid1, *, Ashfaq Ahmad1 , Shahid Ahmed Khan1 , Zahoor Ali Khan2 , Nabil Alrajeh3 , Umar Qasim4

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COMSATS Institute of Information Technology, Islamabad, 44000, Pakistan 2

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Faculty of Engg., Dalhousie University, Halifax, NS B3J 4R2, Canada

CAMS, Dept. of Biomedical Technology, KSU, Riyadh 11633, Saudi Arabia 4

Cameron Library, University of Alberta, Edmonton, AB T6G 2J8, Canada ∗

Correspondence: www.njavaid.com, [email protected] Abstract

In this paper, we comparatively evaluate the performance of home energy management controller which is designed on the basis of heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO) and ant colony optimization (ACO). In this regard, we introduce a generic architecture for demand side management (DSM) which integrates residential area domain with smart area domain via wide area network. In addition, problem formulation is carried via multiple knapsack problem. For energy pricing, combined model of time of use tariff and inclined block rates is used. Simulation results show that all designed models for energy management act significantly to achieve our objections and proven as a cost-effective solution to increase sustainability of smart grid. GA based energy management controller performs more efficiently than BPSO based energy management controller and ACO based energy management controller 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; Heuristic Techniques; Time Of Use Tariff; Inclined Block Rate; Renewable Energy Sources.

I. I NTRODUCTION Traditional power system is inadequate to meet power grid challenges such as reliability, stabil-

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Fig. 1: SG architecture

ity, 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. SG has modernized all sections of conventional power grid system as shown in fig. 1. Specifically, the consumers are now prosumers because they have the ability to sell their generated electricity to the utility. Thus, renewable energy sources (RESs) (e.g., solar, wind, etc.) play a vital role in the concept of SG. Energy utility companies are always interested in profit increment with reduction in peak to average load ratio. On the other hand, prosumers wish to reduce their electricity bills without compromising their comfort level. SG aimed to enhance power efficiency, grid sustainability, storage capacity and customer engagement [2]. Some of the major differences between traditional power grid and SG are summarized in table. I. One of the most important aspects of SG is demand side management (DSM) which is the best solution 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 to avoid major distress and blackout. The benefits of load management are numerous such as reduced number of peak power plants, efficient energy consumption, electricity bill reduction and improved performance of the power grid in term of reliability and flexibility [3]. DR is a responsive action taken by a customer against dynamic

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TABLE I: Brief comparison between traditional grid and SG Infrastructures

Power system

Traditional grid Centralized generation Uni-directional power transmission (utility to consumer) Uni-directional information flow (utility to consumer) DC-DC distribution Low storage capacity Aged metering system

Information technology

No monitoring system

Communication system

Lack of management units Wired technology

Energy sources system

Non- renewable sources (mainly fossil fuel)

Power losses control system

Wastage of electricity due to limited power storage

SG Distributed generation Bi-directional power transmission (utility to (from) consumer) Bi- directional information flow (utility to (from) consumer) Hybrid distribution (DC-DC/DC-AC) Grid energy storage capacity Advanced metering system (advanced metering infrastructure) Smart monitoring (phasor management unit) Information management unit Wired and wireless technologies Both non-renewable and renewable sources (photovoltaic panels, wind turbine, plug-in electric vehicles, etc) Efficient use of electricity minimizes power losses

price models. It offers many financial and operational benefits for electricity utilities, end user, and grid operations. The highly volatile nature of load may threaten the integrity of grid within seconds. Therefore, DR is important to tackle these uncertainties, as it provides flexibility at relatively low rates [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. For example, in [5-7], integer linear programming, mixed integer linear programming and mixed integer non-linear programming are used for electricity cost minimization. Similarly, in [8], authors use convex programming for relatively large number of users to reduce their electricity bills. In [9], [10], integer linear programming and mixed integer linear programming are used to optimally schedule the appliances to minimize the aggregated power consumption. [11], [39] use mixed integer non-linear programming to reduce both electricity bills and aggregated power consumption. 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. In this paper, heuristic optimization techniques are used due

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to their exceptional characteristics; flexibility for specified constraints and low computational complexity [12]. [13] uses evolutionary algorithm (a heuristic approach) to minimize electricity cost for all types of sectors (residential, commercial and industrial). We have chosen three popular heuristic optimization techniques; genetic algorithm (GA), binary particle swarm optimization (BPSO) and ant colony optimization (ACO) due to their self-organization, self-optimization, selfprotection, self-healing and decentralized control system [12]. In literature, various works have been done to enhance the efficiency of DSM using these heuristic optimization techniques. For example, authors in [14], [15], [16] present different models to reduce utility electricity bills using GA. Electricity cost reduction for end users is achieved in [17], [18] with particle swarm optimization (PSO) technique. Signaled PSO (a heuristic approach) is used to reduce aggregated load and execution time with absolute error for number of consumers in [19]. An efficient self-optimizing system for DSM is proposed in [20] to optimally schedule load using ACO technique and [21], [22] investigates congestion problem in SG through DR by applying ACO to minimize cost and maximize user comfort level. Although mentioned work performed well for their specified objectives but still there is a big room for research as up till now comprehensive solution for all the energy crisis of present power grid system is not provoked. Our contributions include designing of state of art smart energy management controller (EMC) for single and multiple homes based on multiple knapsack problems (MKP). Moreover, we have used three heuristic algorithms (GA, BPSO and ACO) to get feasible solution for our designed model and compared their effectiveness in terms of energy consumption pattern, electricity bill, peak to average ratio (PAR), user comfort level, and execution time. Additionally, we have used unique electricity bill calculation method comprising of time of use (TOU) dynamic tariff model with inclined block rate (IBR). One of our major contributions is efficient integration of RESs requiring modifications in heuristic algorithms. This integration encourages consumers to intelligently manage their energy usage. We have used Monte Carlo simulations to tackle randomness in the heuristic algorithms. Results validate that our proposed schemes outperform the existing ones. Performance evaluations of different heuristic algorithms in home energy management system are summarized at the end of the paper. Rest of the paper is organized as follows. Section II briefly describes related work. Next, 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.

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II. R ELATED WORK Many researchers around the world worked to optimally schedule smart appliances. In this regard, some of the papers are discussed as follow: In [23], 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 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 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 [24]. Objective function is formulated by knapsack problem and dynamic programming (DP) 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 [13], 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 [14], 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 nonlinear, therefore they use GA to optimize their problem. Simulation results show that proposed

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model is very effective to reduce PAR and electricity cost. Another DSM model is proposed in [15] 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. Authors in [37], proposed an energy management algorithms for electricity bills and PAR reduction while satisfying end users. They proposed intelligent programmable communication thermostat (iPCT) and conventional programmable communication thermostat (CPCT) by using genetic algorithm (GA) to optimize different classes of appliances. Through simulation results performance of proposed work is validated. An efficient home energy management scheme is proposed in [17] to schedule large number of interruptible loads in the time period of 16 hours. Binary particle swarm optimization (BPSO), which is an extended version of PSO is used to achieve the scheduling objective. The objective is to minimize the electricity bills while satisfying the operational constraints and minimize the frequency to interruptions. The effectiveness of proposed approach is improved by dividing the swarm into number of subswarms. The scheduling technique proven as useful scheme for a relatively challenging scheduling task, and have potential advantages in scheduling widely varied and technically complex interruptible loads. In [18], real-time model for optimal power usage of household appliances is proposed. BPSO algorithm is used to solve the formulated problem by encouraging the participation of both utilities and consumers. By considering the features of the appliances and living habits of customers, the appliances are divided into three categories. Results show the significant performance of proposed scheme for load shifting, energy saving and cost reduction. Authors proposed realistic scheduling mechanism for SG in [38] for end user frustration reduction. For optimal operational time of different appliances and power usage optimization, nature-inspired heuristic algorithm that is BPSO is used. Results show that proposed scheme is cost effective. An efficient heuristic approach is presented in [25] 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

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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. In [26], a home energy management model is designed in which each appliance is operated according to its schedule within predefined time limits. The objective is to reduce utility electricity bills while satisfying operational constraints. GA is used to evaluate the objective function and get optimal start operational time for each appliance to reduce electricity bill and avoid peak formation. It is a comparative study in which GA based energy management model results are compared with simulated annealing and greed method. Simulation results show that GA acts efficiently to reduce cost while minimizing power usage at any instant of time than heuristic techniques. An adaptive energy management model for DSM in residential area is described in [20]. Authors aim to optimize the use of distributed RESs to reduce utility electricity bill. They use ACO as an optimization algorithm to schedule shiftable load and MAPE-K feedback loop as a predictive model to deal with the intermittent nature of RESs. Results show that proposed cost-effective self-optimizing model is able to adapt sudden changes in environmental conditions and optimize the power usage of residential users. Authors in [21] proposed an efficient scheme to manage congestion problem in SG through DR. Their objective is to optimally schedule different generation resources to minimize cost and maximize customer satisfaction. ACO is used as optimization technique to provide benefit to consumers and fuzzy satisfying technique to choose the most feasible solution from the set of pareto optimal solution. Results show that proposed scheme is effective both for generation selection and demand management in the most economical way. In [22], authors investigate congestion cost model in real-time power grid system. They built a congestion factor to control opening of both generation and load sides. Non-linear programming is used to formulate real time congestion problem and cost is minimized on the basis of an optimization algorithm (ACO). Results show that improved model can significantly minimize electricity cost. All the techniques, objectives, achievement (s) and deficiency (ies) mentioned above is summarized in table. II TABLE II: Summarized related work References

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Techniques

Objective (s)

Achievement (s)

Deficiency (ies)

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Efficiently limits Ignore user Electricity

power

consumption and

consumption to

bill reduction

reduce electricity

Residential task scheduling under

comfort level and MKP

dynamic pricing using MKP [23]

integration of RESs bills Satisfy endusers Reduction in

Knapsack problem approach for

by designing Integration of

electricity bills achieving efficient energy

MKP+DP

efficient RESs is ignored

and peak consumption in SG [24]

operational time formation constraints User comfort Electricity bills level and Heuristic

reduction for

Beneficial model

evolutionary

residential,

for both utilities

approach

commercial and

and customers

DSM in SG using Heuristic

integration of

Optimization [13]

RESs are neglected industrial user features Ignore Avoid peak

An Optimal Power Scheduling

congestion Effectively

formation and Method for DR in Home Energy

problem, user reduce PAR and

GA+RTP electricity bill

Management System [14]

comfort level and electricity cost

minimization

integration of RESs User comfort

Utility electricity

level and Effective model

cost A Generic DSM Model for SG

Kp+GA

[15]

+RTP+IBR

integration of for both single

minimization and

RESs are and multiple

peak formation

neglected while users

reduction

achieving desired objectives

Real Time Information Based

Effectively Electricity cost

Energy Management Using

Trade off user reduce electricity

and peak Customer Preferences and

GA

comfort and bills while

formation Dynamic Pricing in Smart Homes

energy preserving user

minimization [37]

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consumption comfort

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Home Energy Management with

Acts potentially

Neglects user

Minimize

to achieve

comfort level and

electricity bills

designed

integration of

objective

RESs

BPSO PSO in SG [17]

Encourage utility User comfort and consumer level and

Energy saving Scheduling of Demand Side

participation to BPSO

integration of

and electricity

Resources Using Binary PSO [18]

maintain balance RESs are not

cost reduction between demand

considered and supply Encourage consumer User frustration Realistic Scheduling Mechanism

Integration of participation and

BPSO

and electricity

for Smart Homes [38]

RESs are not enhance energy

bill reduction

considered management system

Compare

Electricity cost

electricity cost

obtained from

reduction and

heuristic

computational

algorithm is 5%

time for both

optimized than

algorithms

exact algorithm

Analysis three

GA acts

User comfort

algorithms for

effectively than

level and

electricity cost

other to two

integration of

reduction

algorithm

RESs are ignored

designed

Neglects user

cost-effective and

comfort level and

self-optimized

peak formation

model

problem

User comfort Near-Optimal Scheduling of Exact + Residential Smart Home

level and

Heuristic Appliances using Heuristic

integration of

algorithms Approach [25]

RESs are neglected factors

GA + A Genetic Evolutionary Task

Simulated

Scheduling Method for Energy

annealing +

Efficiency in Smart Homes [26]

Greedy method Deals intermittent

Ant-Colony based

ACO + nature of RESs

Self-Optimization for DSM [20]

MAPE-K to reduce electricity bills

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Effective model Congestion ACO + SG Congestion Management

for both

Ignored

generation and

integration of

demand

RESs

problem with Fuzzy

through DR [21]

electricity cost techniques minimization management

Cost Control of the Transmission Congestion

Improved model

Ignored

control with cost

to minimize

integration of

reduction

electricity cost

RESs

Congestion Management in ACO Electricity Systems based on Ant Colony Algorithm [22]

III. M OTIVATION In SG, optimization of energy consumption schedules and user cost minimization are two difficult tasks due to randomness in the energy consumption patterns of end users. In literature, an efficient home energy management controller to reduce utilities electricity bills and PAR is still an issue. Mostly, user comfort level is neglected while reducing electricity bills. Typically, the target of DSM is to efficiently manage the energy schedules such that electricity price is minimized while maximizing user comfort level. In SG, optimization problems are as follow: •

Minimize the electricity bill.

•

Minimize the aggregated power consumption.

•

Minimize both electricity bill and aggregated power consumption.

•

Minimize PAR.

•

Maximize user comfort.

•

Efficient integration of RESs.

Many strategies have been proposed in the previous years to effectively tackle these mentioned problems. Authors in [5-7], present three different techniques: integer linear programming, mixed integer linear programming and integer non-linear programming for electricity bill reduction. However, integration of RES, user comfort and power consumption minimization problems are ignored in these models. Similarly, [8] uses convex programming to deal with large number of consumer for electricity bill reduction. Although this technique gives effective solution however, at the cost of increased computational time. The linear programming based schemes in [9] is effective for the residential area, however, lack of RES integration and non-adaptability

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with dynamic pricing models are its major drawbacks. In the same way in [10], authors use mixed integer linear programming to optimize energy consumption patterns for peak hour shaving to minimize electricity cost with the optimal integration of distributed RESs and energy storage systems (ESSs). Results show that cost saving is not achieved much as expected and user comfort is ignored. Whereas, in [11], both electricity bill minimization and aggregated power minimization problems are investigated using mixed integer linear programming for dual optimization functions. Proposed scheme is implemented for single home but it does not deal with the flexibility of power usage patterns and human behaviors. Thus, to resolve these issues in previously proposed schemes for DSM, we proposed an efficient model using MKP to formulate an objective function for residential area. To evaluate our objective function, heuristic optimization techniques are used. Though these techniques may be trapped on premature convergence but due to their ability to deal with large and complex scenarios within less computational time and less computational complexity we have made their selection [12]. In this paper, we have considered three heuristic techniques: GA, BPSO and ACO to achieve our objectives and compared their results. Despite of great efforts in literature for DSM strategy using these heuristic algorithms, there is still a room for improvement to make system compatible with growing demand of power. As in [14], GA based DSM model is presented to reduce electricity bills for residential area while ignoring user comfort level. Authors used RTP tariff model to calculate electricity bills which is a major drawback of this model because real time data transmission causes great chance for data loss that cause discomfort both for utility and customer. Authors in [17], proposed a model for DSM using PSO techniques to get objective of electricity bill reduction without considering user comfort maximization. Whereas, in [20], authors used ACO and feedback prediction model to enhance efficiency of DSM. They proposed an economical model that optimize power usage without considering user satisfaction parameters. We apply these heuristic optimization techniques in a novel way to enhance the efficiency of DSM. In our focused scenario, household appliances are classified into three categories and problem is formulated by using MKP. TOU tariff model is used to calculate electricity bills for end users and to get feasible solution for designed objective function; we used GA, BPSO and ACO. Also our proposed model significantly integrates RES energy with grid power to deals with global energy crisis and to reduce pressure on natural recourses keeping in view the interest of both players (utilities and consumers).

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IV. 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 smart meters having EMCs to make stable and reliable bi-directional communication between utilities and customers. All elements, such as electrical appliances, sensors, local generation and storage devices 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 such as ZigBee, Z-Wave, Wi-Fi, or a wired (HomePlug) protocol [1] in SG. Simple architecture of DSM is shown in fig. 2. In residential area based DSM, we consider “N” smart homes and

Residen al area domain

SG domain

Distribu on

HAN Smart devices

Opera on Sensors

WAN EMCs

Market

Distributed RESs Service provider ESSs Customers

Two way communica on One way communica on

Fig. 2: Components of DSM

“M” smart appliances as shown in fig. 3. In this model, all smart homes have smart metering system with EMC. End users change their energy usage patterns according to incentive based schemes offered by utilities. Pictorial representation of DSM model for our proposed scheme is depicted in fig. 4. Here, each smart 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 formulate the optimization problem for smart home energy management controller, we have first designed energy consumption model for “N” appliances in the following section.

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Smart meter

Smart meter

EMC

User 1

EMC

User N+1




Power distribu on u lity




User N+2

Smart meter

User N

Smart meter

EMC

EMC

Two way communica on One way communica on Fig. 3: EMC model for residential users

A. Energy consumption model Let A = { a1 , a2 , a3 , . . . , aM } be the set of appliances such that a1 , a2 , a3 , · · · , aM are index of each appliance, respectively in “N” smart homes. Assuming static scheduling time slot “t” as t ∈ T = 1hour(h) for a single day such that T = { 1h, 2h, 3h, · · · , 24h }, then hourly energy consumption demand of an appliance is given as, Ea (t) = { Ea,t1 + Ea,t2 + Ea,t3 + . . . + Ea,t24 }

(1)

where, Ea,t1 , Ea,t2 ,Ea,t3 , · · · ,Ea,t24 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, ET =

24  X M X t=1

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E(ai ,t)



(2)

i=1

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Master control

New data pricing Network interface User energy u
liza
on

Historical appliance data Heuris
c techniques

Next day TOU pricing

Appliance scheduler Appliance schedule

Database manager Aggrega
on process Hourly projected energy demand TOU pricing genera
on

Appliance status

Appliance 1 Appliance manager

Appliance 2 HAN

Appliance M

User Interface User

Fixed device

a

a

a

Shiable device

a

a

a

Elas
c device

a

a

a

System seng and parameters

Two way communica
on One way communica
on

Fig. 4: DSM functional diagram

In energy management problems, accurate results for optimized scheduling problem cannot be possible without considering characteristics of load and user lifestyle. Therefore, “M” appliances are classified into three categories for scheduling purpose according to mentioned points in the next section. B. Load categorization In this section, according to power consumption patterns, time of use and user comfort, total load is divided into three categories; fixed, shiftable and elastic appliances [27]. Detail explanation 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, light, fan, cloth iron, microwave oven, toaster, tv, etc. We represent fixed appliances by “Fed ” and its power consumption as “ν”. If each fixed appliance fed ∈ Fed has power rating “ρfed ”, then total power consumption in each time slot “t”

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of a day is calculated as, νT =

24 X X fed ∈Fed

ρtfed

× χfed (t)



(3)

t=1

where, “χfed (t)” is the state of each fixed appliance in particular time slot “t” and it is given as, χfed (t) =

  1 if appliance is ON

(4)

 0 if appliance is OFF 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 “ϑ”. Each shiftable appliance is characterized by its length of operation that is pre-defined by end users in each particular day. It is denoted by “τsed ” and measured in hour(h). Consumers set start time and end time for each shiftable appliance as, αsed ≤ τsed ≤ βsed

(5)

where, “αsed ” and “βsed ” are the start and end times in hours(h) of a shiftable appliance that are set by end consumers. If each shiftable appliance sed ∈ Sed has power rating factor “ρsed ”, then the total power consumption is calculated as, ϑT =

24 X X sed ∈Sed

 ρtsed × χsed (t)

(6)

t=1

where, “χsed (t)” is the state of each shiftable appliance in particular time slot and it is given as,   1 if appliance is ON χsed (t) = (7)  0 if appliance is OFF 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, start time and end time. These attributes

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are set by the consumer, such that, αeed ≤ τeed ≤ βeed

(8)

where, “αeed ”, “τeed ” and “βeed ” are the start time, length of operation and end times of elastic appliances, respectively and all are measured in hour (h). Power consumption of each elastic appliance “ζeed ” is calculated as follows, ζeted = λeed × ρteed

∀eed ∈ Eed

(9)

The total power consumption is calculated as, κT =

24 X X eed ∈Eed

 ζeted (t) × χeed (t)

(10)

t=1

where, “λ” is used to vary the power quantity level having no unit and “χsed (t)” is the state of each elastic appliance in particular time slot given as,   1 if appliance is ON χeed (t) =  0 if appliance is OFF

(11)

C. Local energy generation The residential users can also use distributed RESs such as PV panels, wind turbines, electric vehicles, etc. The distributed RESs are used to fulfill the energy demand locally or to charge the storage devices. Assume that each home is fitted with PV panel that is capable of generating 50% of total grid power. In this case, consumer becomes prosumer because he/she can generate its own energy. They can also sell the generated RESs energy back to the grid depending upon their agreement with the utility. PV panel generates solar power depending on solar radiations and total estimated radiation varies for every month. Solar power output depends on radiation amount, direction of panels and transfer efficiency. The generated energy in each time slot is characterized as “Ψrt ” and it is calculated by the following expression [28]. Ψr (t) = 10 × √

1 (t − µ)2
exp − 2σ 2 2Πσ

(12)

Where, “µ” is the mean of distribution and “σ” is the variance. The hourly RESs energy must be greater than zero during day time. The daily energy supply from renewable system (PV panel)

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installed by the users is denoted by “Θ” and calculated as, Θ(t) =

24 X

Ψr (t)

(13)

t=1

Let “Θmax ” be the maximum generation capacity of PV panel then available energy must be in following range, 0 ≤ Ψr (t) ≤ Θmax

∀t ∈ T

(14)

If locally generated power is greater than total power demand for appliances in each time slot, then the total power is in negative sign which means that the generated power can be sold back to grid or it can be stored to reduce energy usage during peak hours. In order to be eligible for participation in some agreement with grid to sell negative power back to grid, user must oblige to meet a specific power capacity “Ψrmin ”; Θmax (t) ≥ Ψrmin (t)

∀t ∈ T

(15)

D. Energy storage system When the energy generation exceeds consumption, it is stored. The stored energy is used at high peak hours or it can be used in night, when solar energy is not present. We model ESS with a set “Γ”, where each battery b ∈ Γ. We introduce a binary variable “χby ” that shows charging and discharging status of all “y” number of batteries. A binary variable “χby ” is defined for each time slot, χby (t) =

  1

Charging

(16)

 0 Discharging Charging and discharging rates of a battery are represented by non-negative variables as “rbcy ” and “rbdy ”, respectively. Such variables are bounded by the following constraints [27], rbcy < rbc,max × χby

∀b ∈ B

(17)

rbdy < rbd,max × (1 − χby )

∀b ∈ B

(18)

where, “rbc,max ” and “rbd,max ” are maximum capacity of charging and discharging rates, respectively. There are energy losses during charging and discharging in each battery and its efficiency rate is between 0 < ℵc < 1 and 0 < ℵd < 1 for charging and discharging. A binary variables

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shows that both these operations cannot occur at the same time. Despite the benefits of ESSs, their cost may limit their applicability in real scenarios. E. 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. Let “∆T ” is the total power consumption of all appliances in a particular day, then it is calculated as, ∆T = νT + ϑT + κT

(19)

According to IBR model, the formula used for electricity bill calculation is designed as,    Υ1 0 ≤ ∆T ≤ ∆th1    Υ = Υ2 ∆th1 ≤ ∆T ≤ ∆th2 (20)     Υ ∆ < ∆ 3 th2 T where, “Υ” is the electricity cost charged against power consumption in “cents”. “∆th1 ” and “∆th2 ” are power consumption thresholds and “Υ1 ”, “Υ2 ” and “Υ3 ” are costs for three particular cases. F. Residential users We design our model for three types of users in residential area such as passive, semi-active and active users. We define these categories as, 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”. The energy consumption

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profile for each user is calculated by the following equation: Ei∈P (t) =

24 X

Ei (t)

(21)

t=1

where, i ∈ P consumes electrical energy in time slot “t”. 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”. The energy consumption profile for t ∈ T is calculated as, Es∈S (t) =

24  X

 Es (t) − Θs (t)

(22)

t=1

where, s ∈ S belongs to set of semi-active users and “Θs(t) ” is the solar panel generated energy. 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 “U”. The energy consumption profile for t ∈ T is calculated by the following equation: Eu∈U (t) =

24  X

 Eu (t) − Θu (t) ± Γu (t)

(23)

t=1

where, u ∈ U belongs to set of active users and “Θu (t)” is the solar panel generated energy and “Γu (t)” is the energy stored in batteries. The batteries are charged from RES (not from grid). If “Γu ” is positive, it means battery is charging and if negative, battery is discharging. The conceptual diagram of all types of users is shown in fig. 5. V. P ROBLEM FORMULATION In this work, our main objectives are: to reduce consumer electricity cost by optimizing the energy consumption patterns of end users, to maximize the comfort level of consumers and to maintain balance between demand and supply. Here, we formulate our scheduling problem as optimization problem by using MKP. MKP is a resource allocation problem that consists of “γ” resources (capacities) and set of “M” objects [29]. Whereas, j are the numbers of knapsacks used in designed problem. Following are the assumption that we have considered to map our scheduling problem by using MKP: •

Consider number of appliances as number of objects.

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SG

Smart metering device

EMC

Grid power

Passive users

Grid power + RES

Semi-active users

Grid power + (RES+ESS)

Active users

Power flow

Information exchange

Fig. 5: End users

•

Weight of each object is represented by the energy consumed by each appliance in each time slot. Note that it is independent of “t”.

•

The value of each object in a specific time slot is the cost “Υ” of power consumption of the appliance in that time slot.

•

“j” number of knapsacks are used apply threshold of power capacities “γ” in each time slot.

•

Value of binary variable “χ” can be 0 or 1 depending on the state of electrical appliance.

With the help of these assumptions, consumer can actively participate in energy management schemes to reduce their electricity bill, and for utility side, it is also beneficial because it ensures that the grid is not over stressed. As the total power consumption for all types of appliances should not exceed maximum power capacity in each time slot denoted as “γ(t)”, we introduce

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constraint which limits the power consumption and depends on load profile and its states as, ∆T ≤ γ(t)

(24)

Here, “γ(t)” is the power capacity threshold in each hour that is available amount of power from electricity grid. If this constraint is satisfied, problem of power shortage or blackouts can be removed. A. PAR It is beneficial for the utility and consumer to reduce PAR so that power supply and demand balance can be maintained. We have defined PAR for single user as the ratio of peak load and average load in each time slot. It is represented as “φ” and its mathematical form is as follow,

φ=

max(∆(t)) PT t=1 ∆(t)

1 T

(25)

Therefore, for “N” multiple users, the PAR is written as, φN =

1 T

max(∆(t, n)) PN PT t=1 (∆t,n )) n=1 (

(26)

B. Waiting time It is necessary for residents to set some parameters for each shiftable and elastic appliance. In scheduling problem, we omit fixed appliances because these appliances do not play any role in energy management system and must run with first priority. We assume start time “αa ” and end time “βa ” for each schedulable appliance measured in hours (h) such that αa < βa . The operation time interval (OTI) for each appliance is the time in which it performs its functionality. Let “τa ” be the length of operation (LOT) of an appliances that is required to complete the task. These parameters are needed to set by the resident via user interface and then this information is sent to EMC. Assumed that βa − αa must be greater than or equal to “τa ”, we define operation start time by “ηa ”. As, we already know “αa ”, “βa ”, “τa ” and “χa ” for each appliance but “ηa ” is unknown. Once we get “ηa ”, we can calculate power consumption pattern. Relationship between all these parameters is shown in fig. 6. Now for each appliance, there exists a group of parameters comprising the OTI, LOT, and power consumption values per unit time. “ηa ” must

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βa αa ƞa τa

Fig. 6: Parameters of appliance

be greater than or equal to “αa ” and less than or equal to βa − αa . Therefore, range of “ηa ” is given by, ηa ∈ [αa , βa − τa ]

(27)

The range of “ηa ” is shown in fig. 7. Usually, residents want to finish their work as soon as β α

Range of ƞa τ

β ƞa= βa -τa

α

τ

Fig. 7: Range of operation time

possible. Therefore user comfort depends upon waiting time reduction and cost minimization. There is a trade off between cost and waiting time. When we minimize cost, customer compromises on waiting time and when waiting time reduces customer pays huge cost. Mathematically, waiting time is represented as “ϕ” and for each schedulable appliance and is given as, ϕa =

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ηa − αa βa − τa − αa

(28)

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If an appliance operates at a later time, the later the appliance operates the larger the waiting time will be. The smallest and the largest values of “ϕ” are set between 0 and 1. Assume that for washing machine, a resident sets the parameter OTI as [αw , βw ] and LOT as τw . If it starts working at its starting time that is αw then its ϕw is zero and if it start working at latest time such that βw − τw then its “ϕw ” would be one, as shown in fig. 8. βa αa ƞa=αa

τa

βa αa τa

ƞa=αa βa-τa-αa

βa ƞa=βa-τa

αa

τa

Fig. 8: Waiting time

C. Objective function 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, min

24  X t=1

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A    X w1 · (∆a,t × χa,t ) + w2 ϕa,t )

(29)

a=1

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s.t: αssd ≤ τssd ≤ βssd

(29a)

αsed ≤ τsed ≤ βsed

(29b)

ηa ∈ [αa , βa − τa ]

(29c)

ϕa ≤ 5

(29d)

0 ≤ Ψrt ≤ Θmax

∀t ∈ T

(29e)

rbcy < rbc,max × χby

∀b ∈ B

(29f)

rbdy < rbd,max × χby

∀b ∈ B

(29g)

24 X

(∆a,t × χa,t ≤ γa,t )

(29h)

t=1

χa,t ∈ [0, 1]

(29i)

Here, designed objective function aims to minimize electricity bills while keeping under consideration user comfort level. “w1 ” and “w2 ” are weights of two parts of objective function and their values are w1 , w2 ∈ 0, 1 or w1 + w2 = 1. It shows that either w1 or w2 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 that when value of waiting time “ϕa ” of any appliance is 1 means its OST equal to or greater than 5h then utility companies pay penalty in the form of relaxation is electricity bills. D. Heuristic algorithms Due to highly volatile load behavior of residential users and intermittent nature of RESs, we consider our defined problem as non-linear optimization function and traditional optimization techniques in [5-11] can not handle the complexity of our proposed model due to their nonflexible nature. Therefore, we apply heuristic algorithms (GA, BPSO and ACO) to solve our designed MKP. These algorithms are similar due to population based search methods. They move from one population to another population in number of iterations with improvement using a combination of deterministic and probabilistic rules. In the following subsections, we discuss some of the latest research works of GA, BPSO and ACO as an optimization solutions.

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1) GA: It is most suitable for complex non-linear models where location of the global optimum is a difficult task. Due to its probabilistic nature for development of solution, GA does not guarantee optimality even when it may be reached [30]. As in [16], a combined model of RESs and ESSs is proposed to minimize electricity bills for residential, commercial and industrial areas. Authors use probabilistic model to design their optimization function and optimal solution is obtained from GA technique. RTP is used for electricity pricing model. Results show favorable effects for designed model but in our proposed model, we used GA in more promising manner to achieve our objectives: electricity cost reduction, PAR minimization, maximizing user comfort and optimal integration of RESs. In comparison to [16], we used MKP to balance the demand and supply capacity model and to optimize our objective function, GA is used. Furthermore, combined TOU with IBR pricing model is used instead of RTP because in real time pricing model, chances of data loss are increased due to congestion problem. Another model to improve the efficiency of DSM is proposed in [14]. Authors investigate electricity cost minimization and peak formation problem to make DSM efficient. They defined appliance scheduling problem for power consumption as optimization problem. GA is used to get optimal solution subject to cost minimization and PAR reduction and for electricity pricing model, they used RTP tariff model with IBR. In comparsion to [14], our proposed solution is more effective due to its unique implementation. In our model, grid capacity is predefined by using MKP to maintain balance between generation and demand curve. To give compromising results for user satisfaction, we used GA in more appropriate manner. However, they use RTP with IBR which is not suitable for electricity bill calculation due to increased information loss chances during high data transfer rates and we use TOU tariff model with IBR in which date loss problem is diminished. Whereas, we used PV panel to deal with greenhouse gas emission problem that they totally ignored in their proposed scheme. Authors in [15], proposed GA based home energy management controller for single home in residential area. RTP is used for electricity bill calculation. Results show effectiveness of this model but compared to our model, they ignore some parameters of DSM model. In our paper, detailed GA based energy management controller (GA-EMC) model is shown in algorithm 1, which is improved form of algorithm in [15]. Objective function (refer eq. 29) and its constraints (refer eq. 29a to eq. 29i) are used to find feasible solution. Users input initial parameters (“αa ”, “βa ”, “τa ” and “ρa ”) for all appliances whereas we treat “η” as variable quantity. GA creates a random population initially that consists of certain number of chromosomes represented by binary string as ON/OFF status

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of each appliance. Each chromosome is evaluated using eq. 29. TOU with IBR is embedded as electricity pricing scheme and PV panels model is used as distributed RES to achieve our objectives. Key modifications that we have implemented in GA algorithm [15] to achieve our objectives and its expected outcome (s) are mentioned in table. III. TABLE III: Modification in GA algorithm Enhancement mode MKP capacity factor (refer eq. 29a to 29i) Combined model of TOU and IBR (steps 8, 9, ... ,14) Integration of RES energy model (steps 22, 23,..., 27)

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Expected Outcome (s) Limit energy consumption within certain range Reduce electricity bills PAR reduction Reduce peak power plants Reduction in green house gas emission Maximize consumer participation Further minimized electricity cost

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Algorithm 1 GA-EMC algorithm 1: Initialize all parameters (αa ,βa ,τa ,ρa ) 2: For all users n ∈ N do 3: For all appliances a ∈ A do 4: For all time slots t ∈ T do 5: Randomly generate population represents the patterns of appliances. 6: For p=1:popsize 7: Each individual evaluate the fitness function using eq. 29 8: F=f itnessf unction 9: if (F (p) < F (p − 1))&&(E(t) < γ(t)) then 10:

F (p) = F (p)

11:

if (ϕa using(28) ≥ 5)&&(η ≤ τa ) then appliance is in ON state

12: 13:

else wait untill low peak hours

14: 15:

end if

16: else 17:

F (p) = F (p − 1)

18: end ifENDFOR 19: Generate new population 20: Select pair a, b by Roulette selection criteria 21: if Pc > rand then 22:

crossover(a, b)

23: end if 24: if Pm > rand then 25:

mutate(a, b)

26: end if 27: popnew(popsize,N) 28: Repeat until stopping criteria 29: if E(t) is high then 30:

Θ(t) energy used

31: else 32:

E(t)

33: end if 34: Return best individuals

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2) BPSO: BPSO becomes the prominent evolutionary approach to solve global optimization problems due to its ability to handle non-differential, non-linear multimodal function, parallel behavior, ease of implementation and good convergence properties [31]. Recently, many researches have proposed to make efficient energy management controller using BPSO. In this regard, a real-time appliance usage model is proposed in [17]. Authors use BPSO technique to achieve their objectives; electricity bill reduction, peak shaving, valley filling and demand curve smoothing. They categorized domestic appliances on the basis of characteristics of appliances and living habits of end users. TOU tariff pricing model is considered as a billing model for electricity cost calculation. In our work, we categorized household appliances on the basis of time of usage and appliance power consumption patterns. Combined pricing model, TOU with IBR is used to avoid peak formation and overflow problem. To make system more efficient, we used PV panels to reduce electricity bills and avoid environmental pollution that is totally ignored in [17]. Similarly, in [32], another model is proposed for energy management in DSM based on BPSO. Main objective of this work is to minimize electricity cost for residential area by scheduling shiftable load. They ignore user comfort level while investigating DR program and use TOU pricing model to calculate electricity bills for end users. However, in our model, we formulate our objection function by MKP techniques and BPSO is used to evaluate our designed optimization function. We used TOU with IBR to avoid peak formation. Thus, our proposed model gives more significant solution for electricity bill minimization, PAR reduction and user comfort with optimal integration of RESs. All steps of our proposed model is shown in algorithm. 2. Compared to [31], we modified BPSO according to customer needs. Feasible operation time η is calculated by evaluating objective function (refer eq. 29) and its constraints (refer eq. 29a to eq. 29i). Each particle in the generation is represented by a binary string denoted as states of appliances. These particles are updated by individual velocity and particle position as in [31]. Our proposed model is applicable for single and multiple homes in residential areas. In table. IV, enhancement points and its expected outcomes for BPSO algorithms are mentioned.

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Algorithm 2 Improved Algorithm of BPSO-EMC 1: Initialize all parameters (αa ,βa ,τa ,ρa ) 2: For all users n ∈ N do 3: For all appliances a ∈ A do 4: For all time slots t ∈ T do 5: Randomly generate population 6: Initialize population of size S randomly 7: Initialize particles velocities to 0 8: Initialize individual best to current population 9: Initialize global best to Fbest 10: while Maximum number of generations and min error not reached do 11: For Each individual S 12: For Each individual update velocity of each particle refer [31] 13: For Each individual update position of each particle refer [31] 14: if vmax < 4 then 15: v←4 16: end if 17: if vmin < −4 then 18: v ← −4 19: end if 20: For Each bit 21: if rand < 1+e1−v then 22: bit ← 1 23: else 24: bit ← 0 25: end if 26: if ∆a < ∆1 then 27: calculate electricity bill using Υ1 28: else {∆1 < ∆a < ∆2 } 29: calculate electricity bill using Υ2 30: else {∆a > ∆2 } 31: calculate electricity bill using Υ3 32: end if 33: if Υ(t)is high peak hour then 34: calculate ϕa using (28) 35: else 36: start an appliance 37: end if 38: if Υ(t)is high peak hour then 39: calculate ϕa using (28) 40: else 41: start an appliance 42: end if 43: repeat until iteration end 44: Evaluate particle using (29) 45: if F best < F pbest then 46: localbest ← particle 47: end if 48: if F pbest < F gbest then 49: globalbest ← particle 50: end if 51: using Θ(t) when electricity bill is high 52: if ∆(t)is high then 53: Θ(t) energy 54: else 55: ∆(t) 56: end if 57: end while August 2, 2016

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TABLE IV: Modification in BPSO algorithm Enhancement mode MKP capacity factor (refer eq. 29a to eq. 29i) Combined model of TOU and IBR (steps 26 to 37) Integration of RES energy model (steps 39 to 44)

Expected Outcome (s) Balance between demand and supply Reduce electricity bills PAR reduction Reduce peak power plants Reduction in green house gas emission Maximize consumer participation Further minimized electricity cost

3) ACO: 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 self-organization [20]. In literature, ACO is used for DSM in many ways. For-example, authors in [22], 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 [33], linear programming is used to designed the optimization function. Refer to [34], 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. Major modifications and possible outcomes in ACO algorithm in contrast to [34] are given in table. V

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TABLE V: Modification in ACO algorithm Enhancement mode

Expected Outcome (s)

MKP capacity factor

Balance between demand

(refer eq. 29a to eq. 29i)

and supply

Model of TOU and IBR

Reduce electricity bills

(steps 9 to 15)

and PAR reduction Reduce peak power plants

Integration of RES model

Reduction in greenhouse gas emission

(steps 25 to 30)

Maximize consumer participation Minimized electricity cost

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Algorithm 3 : Improved Algorithm of ACO-EMC 1: Initialize all parameters (αa ,βa ,τa ,ρa ) 2: For all users n ∈ N do 3: For all appliances a ∈ A do 4: For all time slots t ∈ T do 5: Randomly generate ant population 6: while Max. # of iterations and min error not reached do 7:

For Each individual ant update pheromone refer [34]

8:

For Each individual ant evaluate the objective function using (29)

9:

if ∆a < ∆1 then

10: 11: 12: 13: 14:

calculate electricity bill using Υ1 else {∆1 < ∆a < ∆2 } calculate electricity bill using Υ2 else {∆a > ∆2 } calculate electricity bill using Υ3

15:

end if

16:

if Υ(t)is high peak hour then

17:

calculate ϕa using (28)

18: 19:

else start an appliance

20:

end if

21:

local update pheromone for each ant refer [34]

22:

choose best solution so far

23:

global update pheromone for each ant refer [34]

24:

repeat until iteration end

25:

using Θ(t) when electricity bill is high

26:

if ∆(t)is high then

27: 28: 29: 30:

Θ(t) energy else ∆(t) end if

31: end while

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VI. S IMULATIONS AND RESULTS To evaluate different performance metrics of EMC, we conduct extensive simulations in MATLAB. In these simulations, we compare our objectives: electricity bill reduction, energy consumption pattern, PAR reduction, user comfort level and optimal integration of RESs (PV panels) by using different heuristic algorithms; GA, BPSO and ACO. Subject to fair comparison, we used TOU tariff model of Jemena Electricity Networks (VIC) Ltd [35, 36] for residential area with IBR. According to this model, time horizon of 24 hours is divided into three periods as shown in fig. 9. Peak hours are from 3 PM to 9 PM in local time weekdays; shoulder hours are 7 AM to 3 PM and 9 PM to 10 PM in local time weekdays and 7 AM to 10 PM in local time weekends while off peak hours are 10 PM to 7 AM local time all days. Price rate for the peak hours, shoulder peak hours and off peak hours are 14.884 cent/kWh, 9.298 cent/kWh and 4.370 cent/kWh. The purpose of using dynamic pricing model instead of fixed pricing schemes is to enable customers to make informed decisions that can be beneficial for them in terms both of electricity bill reduction and comfort level. These TOU tariff model prices are readily available to customers having advance metering infrastructure.

16 TOU prices

Cost (cent/kWh)

14 12 10 8 6 4 2 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. 9: TOU Tariff Model

For simulations, we design a model for residential area in which each home is equipped with 10 smart appliances and 4 end users. These appliances are further characterized into

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fixed, shiftable and elastic appliances. Different parameters of all appliances can be defined through end users directly or can be obtained from learning algorithms. In this paper, different parameters are obtained from users in advance. Appliances with their parametric values that are used in simulations are shown in table. VI, table. VII, and table. VIII, respectively. In table. VI, 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 of TABLE VI: Parameters of Fixed Appliances Appliances Lighting Fans Clothes iron Microwave oven Toaster Coffee maker

ρa (kWh) 0.6 0.75 1.5 1.18 0.5 0.8

appliances; shiftable and elastic appliances are known as schedulable appliances. As, in table. VII, 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. VIII. TABLE VII: 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

To evaluate the performance of GA-EMC, BPSO-EMC and ACO-EMC, it is required to set important parameters of these heuristic algorithms. Parameters of GA-EMC, BPSO-EMC and ACO-EMC are given in table. IX, table. X and table. XI, respectively. When we apply these algorithms on our designed objective function, execution time is different depending on some characteristics. Execution time of an algorithm is the time in which an

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TABLE VIII: 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

TABLE IX: GA parametric list Parameters Population size Selection Elite count Crossover Mutation Stopping criteria Max. generation

Values 200 Roulette wheel 2 0.8% 0.2% Max. generation 800

algorithm completes its functionality. BPSO executes in more time than GA and unscheduled EMC and ACO takes more time to complete its functionality than BPSO, GA and unscheduled EMCs. Execution time for all models is shown in table. XII. A. Energy consumption pattern and Electricity bill reduction Let the knapsack capacity of power grid is 20 kWh for each time slot per day. To integrate distributed energy sources, we have considered PV panel as a source of renewable energy and batteries as storage system. We use solar panel for power generation which meets 50% of the total load demand. Each smart house is equipped with 1-kW PV arrays. This translates 500 W to 10 kW energy generation capacities. The purpose of integrating RESs with GA-EMC, BPSOEMC and ACO-EMC is to reducing greenhouse gas and to give further advantage to end users by minimizing their electricity bills. Energy consumption pattern using GA-EMC, BPSO-EMC and ACO-EMC without or with RES is shown in fig. 10. It is shown in fig. 10 that maximum energy consumption value are limited to 19.4250 kWh, 18.6750 kWh, 19.4250 kWh, 19.4250 kWh, 18.6450 kWh, 18.8250 kWh and 18.2450 kWh for without EMC, GA-EMC, BPSO-EMC, ACOEMC, GA-EMC (RES), BPSO-EMC (RES) and ACO-EMC (RES), respectively. It is concluded

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TABLE X: BPSO parametric list Parameters Swarm size Max. velocity Min. velocity Local pull (c1 ) Global pull (c2 ) Initial momentum weight Final momentum weight Stopping criteria Max. iteration

Values 200 4 m/s -4 m/s 2N 2N 1.0 Ns 0.4 Ns Max. iteration 600

TABLE XI: ACO parametric list Parameters Ant quantity Pheromone intensity factor (α1 ) Visibility intensity factor (β1 ) Evaporation rate Trail decay factor Stopping criteria Max. iteration

Values 10 2 6 5 0.5 Max. iteration 600

that energy consumption pattern of all models are under predefined knapsack capacity of grid. It is important to notice that GA-EMC acts slightly better than BPSO-EMC and ACO-EMC whereas, ACO-EMC (RES) performed well than others by reducing maximum energy consumption value. During high energy consumption hours, consumers use energy from RES and ESS to further minimize utility electricity bills. Results show that electricity consumption and loses can be further optimally reduced when consumers smartly handle their electricity usage and accomplish their energy needs by using energy in an intelligent way. The maximum value of electricity bill in unscheduled model is 266.3492 cent as shown in fig. 11. It is reduced to 81.6097 cent in the case of GA-EMC while it is reduced from 266.3492 cents to 98.7183 cent in BPSOEMC and to 114.2536 cent in ACO-EMC. During peak hours (16-22), sufficient electricity cost reduction is shown for all designed models (GA-EMC, BPSO-EMC and ACO-EMC). GA-EMC acts more effectively than BPSO-EMC and ACO-EMC in achieving our designed objective of electricity cost reduction due to its unique parameters (crossover and mutation) and BPSO-EMC

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TABLE XII: Execution time Execution time Without EMC GA-EMC PSO-EMC ACO-EMC

Values (seconds) 0.0983 1.0191 24.1933 77.7434

20

Without EMC With GA−EMC With BPSO−EMC With ACO−EMC With GA−EMC(RES) With BPSO−EMC(RES) With ACO−EMC(RES)

15

10

Energy consumption (kWh)

Energy consumption (kWh)

20

5

0

15

5

0

2

4

6

8

10

12 14 Time (hours)

16

18

20

22

24

Without EMC With GA−EMC With BPSO−EMC With ACO−EMC With GA−EMC(RES) With BPSO−EMC(RES) With ACO−EMC(RES)

10

2

4

6

8

10

12 14 Time (hours)

16

18

20

22

Fig. 10: Energy consumption (kWh)

acts slightly better than ACO-EMC due to its characteristics of local and global exploration. When we integrate RES with these models electricity bills is further reduced due to consumer participation. GA-EMC, BPSO-EMC and ACO-EMC with RES models show maximum values at 75. 4787 cent, 90.4918 cent and 98.0409 cent as in fig. 11. Now, total electricity bill reduction

300 Without EMC With GA−EMC With BPSO−EMC With ACO−EMC With GA−EMC(RES) With BPSO−EMC(RES) With ACO−EMC(RES)

Electricity bill (cent)

250 200 150 100 50 0

2

4

6

8

10

12 14 Time (hours)

16

18

20

22

24

Fig. 11: Electricity bill (cent)

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38

per day of a single home for all models is shown in fig. 12. Electricity bill reduction in the case of GA-EMC, BPSO-EMC ans ACO-EMC is 48.79%, 40.43% and 28.26% respectively. This shows that GA-EMC is more cost-efficient than BPSO-EMC and ACO-EMC. All above results

Fig. 12: Electricity bill reduction per day

are for single home but what happens if we increase number of homes. To see the effect, we have considered 50 homes for which energy consumption and electricity cost reduction are measured in a particular day. From fig. 13, it is verified that our designed models achieved significant results. As these controllers designed to optimize starting time of all appliances while satisfying constraints of objective function in 24 hours so that residential users gets benefic by reducing their electricity bills and utilities get advantage by keeping demand under power capacity of power grid. B. PAR Performance of all the designed models (GA-EMC, BPSO-EMC and ACO-EMC) with respect to PAR reduction is shown in fig. 14. It shows that PAR is significantly reduced for GA-EMC, BPSO-EMC and 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 models effectively tackle the peak formation problem. PAR curves for GA-EMC, BPSO-EMC and ACO-EMC describe that power consumption of appliances are optimally distributed in 24 hours without

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Electricity bill (cent)

3000 2500 2000

Without EMC With GA−EMC With BPSO−EMC With ACO−EMC

1500 1000 500 0

5

10

15

20 25 30 Number of homes

35

40

45

50

Fig. 13: Electricity bill per day

creating peak in peak hours (16-22) of a day. BPSO-EMC has high PAR than ACO-EMC and GA-EMC and GA-EMC is more effective in PAR reduction due to its ability to generate new population of more feasible solution using crossover and mutation. Peak formation is a major drawback in traditional electric power system as it causes customer to pay high electricity bills and utility suffers high demand that causes blackout or load shedding. We have used combined model of TOU and IBR for electricity billing to avoid peak formation via giving information to consumers. The performance of these algorithms in our scenario is improved due to power capacity factor that cause utilities to fulfill the demand of customers and gives chance end user to reduce electricity bills. 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 (refer eqs. 29c and 29d) on the objective function (refer eq. 29), we have enhanced the performance of EMC in terms of user comfort and electricity bill reduction. In fig. 15, 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

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250 Without EMC With GA−EMC With BPSO−EMC With ACO−EMC

PAR curve

200 150 100 50 0

2

4

6

8

10

12 14 Time (hours)

16

18

20

22

24

Fig. 14: PAR curve

time for all models. Performance of GA-EMC is much better than other due to minimize effect of tradeoff. The purpose of scheduling algorithm to delay the operation of any appliance is to

Electricity cost (cent)

35 Without EMC With GA−EMC With BPSO−EMC With ACO−EMC

30 25 20 15 10 5 0

0.1

0.2

0.3

0.4 0.5 0.6 Waiting time rate

0.7

0.8

0.9

1

Fig. 15: Possible trade off between electricity cost and waiting time

optimize the system according to the designed objective function. When energy consumption of an appliance is more than the power capacity of a particular hour or during high peak hour, appliance scheduler shifts the appliance to another time slot. By ignoring waiting time factor, optimized scheduling can not be achieved. On the other hand, our proposed scheme gives an effective solution. The total electricity bill reduction per day for all designed models with RES is shown in fig. 16. Here, it is clear that GA-EMC performs more effectively than BPSO-EMC

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TABLE XIII: Summarized results Cases

Total load (kWh/day)

Total cost (cent/day)

PAR reduction

Without EMC GA-EMC BPSO-EMC ACO-EMC

258 258 258 258

2201 1127 1311 1579

244.6747 81.8808 95.2281 127.5380

Cost reduction (%) without RESs 48.79 40.43 28.26

Cost reduction (%) with RESs 65 57 52

and ACO-EMC with the integrated RES model. In the case of GA-EMC with RES, the per day electricity bill is 752 cents, whereas, in case of BPSO-EMC with RES, it is 940 cents and in ACO-EMC, it is 1046 cents. Therefore, electricity bill reduction using GA-EMC with RES is 65%, BPSO-EMC with RES is 57% and ACO-EMC with RES is 52%. Results of all models

Fig. 16: Electricity bill per day

are significant to achieve our goals, however, GA-EMC based model is proven more effective than the BPSO-EMC and ACO-EMC models. All the results are summarized in the following table XIII. D. Parametric tuning for all models In this section, tuning effects for different parameters of three heuristic techniques (GA, BPSO and ACO) on our designed model is discussed in detail. Results are summarized in table. XIV, table. XV and table. XVI.

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Table. XIV, shows how effectively power consumption, electricity bill with RES, PAR reduction, electricity bill with RES and execution time values are changed when parameters of GA altered. In our work, population size, maximum generation, crossover and mutation parameters are analyzed at different values while keeping others constant as defined in table IV. We consider three values of population size (200, 1000 and 2000), four values for maximum generation (2000, 1500, 1000 and 800) and three values for each crossover (1, 0.8 and 0.6) and mutation (0, 0.2 and 0.4). After evaluating our designed objective function for each value of considered parameters, we get results that are summarized here. Maximum value of scheduled load is in the range of 12.7480 kWh to 18.6750 kWh that is less than 19.4550 kWh which is maximum value unscheduled load and also in limits of assumed power grid capacity of 20 kWh whereas, maximum value of electricity bill is in the range of 55.6957 cent to 84.4787 cent that is optimized results than unscheduled cost that is 266.3492 cents. For PAR reduction, its values are change between 38.4030 and 215.6530. In the way, electricity cost per day without using RES is 1.125 × 103 cent that is reduced cost than 2.2011 × 103 cent and when we integrate RES with our designed model than electricity cost reduction is between 5.5715 × 102 cent and 1.0512 × 102 cent. Execution time is greatly effected when population size and maximum generation is changed. As, it slightly increase with increase in number of population size and generation. It is clearly notice from our tables that performance of GA-EMC is more significant than other two models due to its evolutionary nature. As in GA-EMC, population is randomly generated depending upon nature of problem whereas, selection is done using designed objective function and roulette wheel criteria, moreover, crossover and mutation plays key role to generate new population which is fitter than the older one. Thus, GA-EMC gives more optimized results than other two model within less time. Performance evaluation for different parameters of BPSO-EMC is summarized in table. XV. For analysis, we consider three different values for swarm size (10, 20 and 40), three values for maximum iteration (1800, 1500 and 600) and four values for each local pull factor c1 (0, 1, 2 and 3) and global pull factor c2 (4, 3, 2 and 1) and remaining parameters are mentioned in table V. It is proven from results that BPSO-EMC performs well but not as good as GA-EMC. Maximum optimized energy consumption values for BPSO-EMC model are between 15.1543 kWh and 19.3106 kWh that obey power capacity of grid whereas, maximum

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TABLE XIV: Parameter Evaluation for GA Pop. size

Max. gen

Cros. (%)

Mut. (%)

Max. energy (kWh)

Max. cost (cent)

Max. PAR

Cost (cent/day) without RES

Cost (cent/day) with RES

Exe. time (sec.)

2000

1 0.8 0.6

0 0.2 0.4

13.0650 17.1450 18.6450

57.0940 74.9236 81.4787

44.9548 85.9937 104.1863

1.1275 × 103 1.1275 × 103 1.1275 × 103

1.0512 × 103 7.5288 × 102 8.0158 × 102

2.4326 2.3215 2.2022

1500

1 0.8 0.6

0 0.2 0.4

17.1450 18.6450 17.1450

74.9236 81.4787 74.9236

81.5619 81.7315 115.0993

1.1275 × 103 1.1275 × 103 1.1275 × 103

6.0303 × 102 9.6454 × 102 6.0503 × 102

1.8583 1.7596 2.0181

1000

1 0.8 0.6

0 0.2 0.4

17.1450 17.1450 18.6450

74.9236 74.9236 81.4787

58.9935 71.0060 178.6347

1.1275 × 103 1.1275 × 103 1.1275 × 103

7.5288 × 103 7.5288 × 102 8.8306 × 102

1.2520 1.2366 1.2059

800

1 0.8 0.6

0 0.2 0.4

12.7450 18.6750 18.6450

55.6957 81.6097 81.4787

38.4030 81.8808 81.4787

0 0.2 0.4

17.1450 18.6450 18.6450

74.9236 84.4787 81.4787

82.0499 215.6530 158.4000

6.2624 × 102 7.5288 × 102 7.2011 × 102 7.5288 × 102 6.2840 × 102 7.1670 × 102

1.0403 1.0191 1.0019

1 0.8 0.6

1.1275 × 103 1.1275 × 103 1.1275 × 103 1.1275 × 103 1.1275 × 103 1.1275 × 103

2000

1500

1 0.8 0.6

0 0.2 0.4

17.1450 18.6450 18.6450

81.4750 74.9236 81.4787

66.4213 136.2192 124.1276

1.1275 × 103 1.1275 × 103 1.1275 × 103

7.2011 × 102 6.7796 × 102 5.5715 × 102

2.2721 2.1938 2.3219

1000

1 0.8 0.6

0 0.2 0.4

18.6450 18.6450 18.6450

81.4787 81.4787 81.4787

81.7315 110.8996 158.4000

1.1275 × 103 1.1275 × 103 1.1275 × 103

8.8306 × 102 6.3863 × 102 7.0647 × 102

1.5643 1.5487 1.4883

800

1 0.8 0.6

0 0.2 0.4

17.1450 18.6450 18.6450

74.9236 81.4787 81.4787

75.1562 94.7048 158.4000

1 0.8 0.6

0 0.2 0.4

18.6450 17.9250 18.6450

81.4787 78.3323 81.4787

81.7315 120.3357 156.7356

9.0273 × 102 9.6454 × 102 8.8697 × 102 7.2011 × 102 7.2928 × 102 6.3522 × 102

1.3744 1.3384 1.3195

2000

1.1275 × 103 1.1275 × 103 1.1275 × 103 1.1275 × 103 1.1275 × 103 1.1275 × 103

1500

1 0.8 0.6

0 0.2 0.4

17.9250 18.6450 18.6450

78.3332 81.4787 81.4787

90.4732 137.4787 212.5796

1.1275 × 103 1.1275 × 103 1.1275 × 103

8.1417 × 102 8.0158 × 102 8.0158 × 102

2.6474 2.6172 3.0554

1000

1 0.8 0.6

0 0.2 0.4

17.1450 18.6450 18.6450

74.9236 81.4787 81.4787

94.4845 110.8936 136.2192

1.1275 × 103 1.1275 × 103 1.1275 × 103

7.5319 × 102 6.3863 × 102 6.2499 × 102

2.1044 2.0605 2.0118

800

1 0.8 0.6

0 0.2 0.4

17.1450 18.8450 18.6450

74.9236 81.4787 81.4787

74.3415 93.5172 127.3058

1.1275 × 103 1.1275 × 103 1.1275 × 103

6.7796 × 102 8.8366 × 102 5.6204 × 102

1.8535 1.8292 1.7825

200

3.7997 3.6981 3.0158

1000

3.2209 3.1092 3.0691

2000

value for cost reduction is in the range of 94.8872 cent to 113.112 cent for different values of parameters. These variations are due to local pull and global pull parameter that directly affects optimization phenomena to evaluate objective function. Similarly, in the case of PAR reduction in which values are reduced as compared to unscheduled model. Electricity cost reduction for a particular day without RESs is 1.3112 × 103 cent and with RES it is furthered. Execution time is highly increases with increase in the number of swarm particles due to execution of step in BPSO for each individual. As, GA is different from BPSO and ACO due to its unique parameters (crossover and mutation) due to which it complete its functionality within less time than BPSO and ACO. While in BPSO, local and global exploration enables the algorithm to give feasible solution while obeying defined constraints (eq. 29a to eq. 29i). Both global exploration and local

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TABLE XV: Parameter Evaluation for BPSO Swarm. size

10

20

40

Max. iter

Loc. pull (c1 )

glob. pull (c2 )

Max. energy (kWh)

Max. cost (cent)

Max. PAR

Cost (cent/day) without RES

Cost (cent/day) with RES

Exe. time (sec.)

1800

0 1 2 3

4 3 2 1

18.5684 17.2285 17.2783 17.9422

104.8874 104.8874 102.9784 105.2652

95.6426 95.1082 135.1126 110.824

1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103

8.4694 × 102 9.3981 × 102 9.4145 × 102 8.6726 × 102

47.6986 47.8157 48.4773 48.1125

1200

0 1 2 3

4 3 2 1

17.5230 19.3106 15.1543 18.4918

100.8872 95.8872 95.7190 100.8872

136.4045 158.1255 180.9130 105.5971

1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103

9.3871 × 102 8.5230 × 102 1.0263 × 102 9.3970 × 102

47.1692 47.1188 57.7714 48.0297

600

0 1 2 3

4 3 2 1

18.8250 19.1240 18.6250 18.8250

105.2852 100.8872 98.7189 105.2652

133.3874 201.4372 95.2281 186.9246

17.4665 18.6450 17.7227 16.8228

100.8872 100.8872 95.6178 95.6178

120.5838 209.2746 190.8179 181.8585

8.5560 × 102 1.0278 × 103 9.4024 × 102 9.3249 × 102 9.4090 × 102 8.5454 × 102 9.4145 × 102 9.3882 × 102

24.0676 27.5235 24.1933 24.1033

4 3 2 1

1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103

1800

0 1 2 3

142.0157 144.9287 143.9084 143.2447

1200

0 1 2 3

4 3 2 1

18.6257 19.1387 16.8228 16.4068

103.9236 111.4748 100.8872 100.8872

203.6886 149.9150 122.7205 216.3967

1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103

7.7692 × 102 7.7166 × 102 8.3566 × 102 9.3511 × 102

96.9877 96.5602 97.2115 98.1963

600

0 1 2 3

4 3 2 1

18.4999 18.7275 17.2525 17.3655

100.7315 100.8872 95.6178 94.8872

131.4721 205.5694 182.0045 131.0050

8.6180 × 102 7.7637 × 102 8.5656 × 102 9.3637 × 102

45.6568 44.9692 45.8085 45.1651

1800

0 1 2 3

4 3 2 1

17.4665 18.3676 17.7227 18.9652

111.4787 102.3123 104.4711 113.1122

156.9369 99.0604 166.8758 154.7958

1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103

7.4135 × 102 6.1031 × 102 6.0769 × 102 8.5437 × 102

287.0919 306.9691 286.2626 472.8304

1200

0 1 2 3

4 3 2 1

17.6810 19.1658 17.5402 18.3967

111.4787 111.4787 113.1122 102.5789

100.4732 97.4787 100.5796 186.1581

1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103

289.2429 439.8685 286.8129 196.6268

600

0 1 2 3

4 3 2 1

18.4999 18.7275 17.2525 16.3655

95.9236 111.4787 113.1122 111.4787

150.8515 114.8936 156.8815 186.3058

1.3112 × 103 1.3112 × 103 1.3112 × 103 1.3112 × 103

6.0996 × 102 6.4260 × 102 6.5605 × 102 8.5077 × 102 6.0605 × 102 5.2804 × 102 6.8863 × 102 8.5663 × 102

95.8205 95.7764 95.2590 95.5248

exploration is affected by updating particles velocity and position. From table. XVI, simulation results for tuning of different parameters are shown. In the case of ACO-EMC model, we have considered two values of ant population (10 and 20), three number of iterations (2000, 1500 and 600), three values for visibility intensity factor (6, 10 and 15) and two trial decay values (0.5 and 1). ACO-EMC acts better than unscheduled models but its performance is not as good as other two models (GA-EMC and BPSO-EMC). Maximum optimized value after evaluating our objective function is in the range of 17.5064 kWh to 19.4250 kWh that is under predefined power grid capacity and electricity bill reduction is between 110.5583 cent and 127.1830 cent that is much less than 266.3492 cent for the cost of unscheduled model. PAR reduction is in the range of 95.5807 to 215.6530. In the case of ACO-EMC, electricity bill reduction is 1.5785×103 cent and with RES it is more reduced to 1.046 × 102 cent. Execution time of ACO-EMC is very

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TABLE XVI: Parameter Evaluation for ACO Ant pop.

Max. iter.

2000

10

1500

600

2000

20

1500

600

Vis. int. (β1 )

Tra. dec. factor

6

0.5 1

Max. energy (kWh)

Max. cost (kWh)

Max. PAR

19.4250 18.6450

127.3309 114.0407

122.3898 137.3747

Cost (cent/day) without RES

Cost (cent/day) with RES

1.5785 × 103 1.5785 × 103

1.4136 × 103 1.4644 × 103

3

2

Exe. time (sec.) 363.8474 282.1364

1.5785 × 10 1.5785 × 103

9.6481 × 10 9.9867 × 102

95.5807 127.5206

1.5785 × 103 1.5785 × 103

1.4644 × 103 9.9021 × 102

292.9011 283.5336

117.4982 127.3309

122.3898 132.6316

1.5785 × 103 1.5785 × 103

9.2592 × 102 6.6726 × 102

208.0962 201.0578

18.6450 19.4250

122.2180 127.3309

215.6530 132.6316

1.5785 × 103 1.5785 × 103

9.1294 × 102 1.0249 × 103

211.9933 203.0963

0.5 1

17.9850 19.4250

117.8917 127.3309

122.7994 132.6316

1.5785 × 103 1.5785 × 103

8.2655 × 102 8.9931 × 102

220.3764 211.6243

6

0.5 1

18.9750 18.2450

114.2536 114.6223

127.5380 124.5747

1.5785 × 103 1.5785 × 103

1.1753 × 103 6.6231 × 102

77.4729 111.5901

10

0.5 1

18.2450 19.4250

111.6226 118.8420

127.5253 132.6312

1.5785 × 103 1.5785 × 103

1.046 × 103 7.0148 × 102

15

0.5 1

18.2450 18.6750

105.9222 114.5663

215.6530 127.5655

6

0.5 1

19.4250 19.4250

118.8421 127.1830

132.6316 132.6316

1.5785 × 103 1.5785 × 103 1.5785 × 103 1.5785 × 103

6.5878 × 102 9.4386 × 102 6.0984 × 102 5.5813 × 102

3

2

10

0.5 1

19.4250 18.6450

118.8421 122.4147

132.6316 127.6530

15

0.5 1

18.3640 18.6450

114.0512 114.0701

6

0.5 1

17.9250 19.4250

10

0.5 1

15

311.5912 253.6215

78.5933 75.3730 77.7434 76.6911

1582.6952 1434.3018

1.5785 × 10 1.5785 × 103

6.5806 × 10 7.6541 × 102

211.0265 132.6316

1.5785 × 103 1.5785 × 103

7.0651 × 102 6.4458 × 102

1621.2112 1455.0140

118.8421 122.2180

132.6316 127.3018

1.5785 × 103 1.5785 × 103

7.0044 × 102 7.8185 × 102

1125.8606 1387.2139

19.4250 17.5046

110.5583 132.3618

132.6316 127.3065

1.5785 × 103 1.5785 × 103

1.1434 × 102 7.6541 × 102

1185.5512 1125.3564

0.5 1

18.6450 19.4250

127.3309 110.5583

211.0265 132.6316

1.5785 × 103 1.5785 × 103

7.6541 × 102 7.8541 × 102

1303.6541 1103.6541

6

0.5 1

18.6450 19.4250

122.2180 118.8421

161.8373 132.6316

1.5785 × 103 1.5785 × 103

6.6411 × 102 6.7255 × 102

622.5746 521.5444

10

0.5 1

18.6450 17.5064

103.6498 132.3618

211.6541 205.1154

1.5785 × 103 1.5785 × 103

9.4933 × 102 9.5487 × 102

15

0.5 1

18.6450 19.4250

122.2180 132.3618

127.5107 211.6541

1.5785 × 103 1.5785 × 103

7.3781 × 102 6.6411 × 102

10

0.5 1

18.6450 17.5046

122.2190 130.3618

215.6341 127.3058

15

0.5 1

18.6450 19.4250

127.5690 127.3309

6

0.5 1

19.4250 18.6450

10

0.5 1

15

1525.2417 1404.2934

419.7896 411.8112 417.8833 401.9631

high than others due to its pheromone update for each ant and number of step for designed algorithm (refer Algorithm. 3) is repeated till convergence to feasible solution. VII. C ONCLUSION AND F UTURE W ORK In this paper, we have presented state of art EMC model for residential energy management system in order to avoid peak formations while focusing on energy utilities electricity bill reduction by preserving user comfort level within acceptable limits. We evaluated our designed objective function by using three heuristic algorithms (GA, BPSO and ACO) and makes comprehensive analysis for all of them. Our proposed model used combine pricing models, TOU

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tariff and IBR model for electricity bill calculation. From the results, it is clearly justified that our proposed model works more efficiently with GA-EMC than BPSO-EMC and ACO-EMC in term of electricity bill reduction, minimizing PAR while considering user satisfaction. GAEMC executes with less execution time than others as GA-EMC>BPSO-EMC>ACO-EMC. Additionally, results of extended model for multiple users are shown to validate our work in terms of relative scalability. 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. We will also work on the different optimization methods so that more accurate data transformation is achieved with in less execution time and computational complexity. R EFERENCES [1] V. Gungor, D. Sahin, T. Kocak, S. Ergut, C. buccella, C. Cecati and G. Hancke, “Smart Grid Technologies: Communication Technologies and Standards”, IEEE Transaction on Industrial Informatics, Vol. 7, No. 4, pp. 529-539, Nov. 2011. [2] M. Hashmi, S. Hanninen, and K. Maki, “Survey of Smart Grid Concepts, Architectures and Technological Demonstrations Worldwide”, IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America), Medellin, pp. 1-7, Oct. 2011. [3] L. Gelazanskas, and K. A. A. Gamage, “Demand Side Management in Smart Grid: A Review and Proposals Forfuture Direction”, Sustainable Cities and Society, Vol. 11, pp. 22-30, Feb. 2014. [4] P. Siano, “Demand Response and Smart Grids A Survey”, Renewable and Sustainable Energy Reviews, Vol. 30, pp. 461478, Feb. 2014. [5] A. Molderink, V. Bakker, M. G. Bosman, J. L. Hurink, and G. J. Smit, “Domestic Energy Management Methodology for Optimizing Efficiency in Smart Grids”, IEEE Bucharest on PowerTech, Bucharest, pp. 1-7, July 2009. [6] T. Sousa, H. Morais, Z. Vale, P. Faria, and J. Soares, “Intelligent Energy Resource Management Considering Vehicle-to-Grid: A Simulated Annealing Approach”, IEEE Transaction on Smart Grid, Vol. 3, No. 1, pp. 535-542, Mar. 2012. [7] J. Soares, T. Sousa, H. Morais, Z. Vale, and P. Faria, “An Optimal Scheduling Problem in Distribution Networks Considering V2G”, IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG), Paris, pp. 11-15 Apr. 2011. [8] K. M. Tsui and S. C. Chan, “Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing”, IEEE Transaction on Smart Grid, Vol. 3, No. 4, pp. 1812-1821, Dec. 2012. [9] Z. Zhu, J. Tang, S. Lambotharan, W. H. Chin, and Z. Fan, “An Integer Linear Programming Based Optimization for Home Demand Side Management in Smart Grid”, IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, pp. 16-20 Jan. 2012. [10] P. O. Kriett and M. Salani, “Optimal Control of a Residential Microgrid”,8th World Energy System Conference, Vol. 82, No. 1, pp. 321-330, June 2012.

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[11] J. Wang, Z. Sun, Y. Zhou, and J. Dai, “Optimal Dispatching Model of Smart Home Energy Management System”, IEEE PES Innovative Smart Grid Technologies, Tianjin, pp. 1-5, May 2012. [12] D. Maringer, “Portfolio Management with Heuristic Optimization”, Springer, 2004. [13] T. Logenthiran, D. Srinivasan, and T. Z. Shun, “Demand Side Management in Smart Grid using Heuristic Optimization”, IEEE Transaction on Smart Grid, Vol. 3, No. 3, pp. 1244-1252, Sept. 2012. [14] Z. Zhao, W. C. Lee, Y. Shin and K. Song, “An Optimal Power Scheduling Method for Demand Response in Home Energy Management System”, IEEE Transaction on Smart Grid, Vol. 4, No. 3, pp. 1390-1400, Sept. 2013. [15] M. A. Khan, N. Javaid, A. Mahmood, Z. A. Khan and N. Alrajeh, “A Generic Demand-Side Management Model for Smart Grid”, International Journal of Energy Research, Vol. 39, No. 7, pp. 954964, June 2015. [16] A. Arabali, M. Ghofrani, M. Etezadi-Amoli, M. S. Fadali, and Y. Baghzouz, “Genetic-Algorithm-Based Optimization Approach for Energy Management”,IEEE Transaction on Power Delivery, Vol. 28, No. 1, Jan. 2013. [17] Y. Zhou, Y. Chen, G. Xu and Q. Zhang, “Home Energy Management with PSO in Smart Grid”,Industrial Electronics (ISIE), IEEE 23rd International Symposium, Istanbul, pp. 1666 - 1670, June 2014. [18] M. A. A. Pedrasa, T. D. Spooner and I. F. MacGill, “Scheduling of Demand Side Resources Using Binary Particle Swarm Optimization”, IEEE Transactions on Power Systems, Vol. 24, No. 3, pp. 1173 - 1181, Aug. 2009. [19] J. Soares, M. Silva, T. Sousa, Z. Vale, and H. Morais, “Distributed Energy Resource Short-Term Scheduling using Signaled Particle Swarm Optimization”, 8th World Energy System Conference, Vol. 42, No. 1, pp. 466-476, June 2012. [20] T. Dethlefs, T. Preisler and W. Renz, “Ant-Colony based Self-Optimization for Demand-Side-Management”, Conference in SmartER Europe (E-world energy and water), Essen, pp 1-8, Feb. 2015. [21] J. Hazra, K. Das and D. P. Seetharam, “Smart Grid Congestion Management through Demand Response”, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm), Tainan, pp. 109 - 114, Nov. 2012. [22] B. Liu, J. Kang, N. Jiang and Y. Jing, “Cost Control of the Transmission Congestion Management in Electricity Systems based on Ant Colony Algorithm”, Energy and Power Engineering, Vol. 3, No. 1, pp. 17 - 23, Feb. 2011. [23] N. Kumaraguruparan, H. Sivaramakrishnan and S. Sapatnekar, “Residential task scheduling under dynamic pricing using the multiple knapsack method”, IEEE Conference, Washington, DC, pp. 1-6, Jan. 2012. [24] O. A. Sianaki, O. Hussian and A.R. Tabesh, “A Knapsack Problem Approach for Achieving Efficient Energy Consumption in Smart Grid for End-user Life Style”,IEEE conference, Waltham, MA, Sept. 2010. [25] C. Ogwumike, M. Short and M. Denai, “Near-Optimal Scheduling of Residential Smart Home Appliances using Heuristic Approach”, IEEE International Conference on Industrial Technology (ICIT), Seville, pp. 3128 - 3133, Mar. 2015. [26] H. Miao, X. Huang and G. Chen, “A Genetic Evolutionary Task Scheduling Method for Energy Efficiency in Smart Homes”, International Journal of Electrical Engineering, Vol. 7, No. 5, pp. 1827-6660, Oct. 2012. [27] A. Barbato and A. Capone, “Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey”, Energies 2014, Vol. 7, No. 9, pp. 5787-5824, Sept. 2014. [28] C. Monteiro, T. Santos, L. A. F. Jimenez, I. J. R. Rosado and M. S. T. Olarte, “Short-Term Power Forecasting Model for Photovoltaic Plants based on Historical Similarity”, Energies 2013, Vol. 6, No. 5, pp. 2624-2643, May 2013. [29] H. Kellerer, U. Pferschy, and D. Pisinger, “Knapsack Problems”. Springer, 2004. [30] S. Mardle and S. Pascoe“An Overview of Genetic Algorithms for the Solution of Optimisation Problems”, CHEER, Vol. 13, No. 1, pp. 16-20, Mar. 1999.

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[31] H. M. Lugo-Cordero, A. Fuentes-Rivera, R. K. Guha, and E. I. Ortiz-Rivera, “Particle Swarm Optimization for Load Balancing in Green Smart Homes”, IEEE Congress of Evolutionary Computation (CEC), New Orleans, LA, pp. 715 - 720, June 2011. [32] T. Remani, E.A. Jasmin and I. T. P. Ahamed, “Load Scheduling with Maximum Demand Using Binary Particle Swarm Optimization”, IEEE International Conference on Technological Advancements in Power and Energy, Kollam, June 2015. [33] I. K. Yu and Y. H song, “A Novel Short-term Generation Scheduling Technique of Thermal Units using Ant Colony Search Algorithms”, International Journal of Electrical Power and Energy Systems, Vol. 23, No. 6, pp. 471479, Aug. 2001. [34] S. Rahim, S. A. Khan, N. Javaid, N. Shaheen, Z. Iqbal and G. Rehman, “Towards Multiple Knapsack Problem Approach for Home Energy Management in Smart Grid”, 8th IEEE International Conference on Network-Based Information Systems, Taipei, Taiwan, pp. 48-50, sept. 2015. [35] http://jemena.com.au/supply-interruptions/electricity(Last accessed: sep 20, 2015) [36] “Jemena Electricity Networks (VIC) Ltd - Network Tariffs For The 2015 Calendar Year (Exclusive of GST)” [37] M. B. Rasheed, N. Javaid, M. Awais, Z. A. Khan, U. Qasim, N. Alrajeh, Z. Iqbal and Q. Javaid, “Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes”, Energies 2016, Vol. 9, No. 7, pp. 542, July. 2016. [38] D. Mahmood, N. Javaid, N. Alrajeh, Z. A. Khan, U. Qasim, and Imran Ahmed, and M. Ilahi, “Realistic Scheduling Mechanism for Smart Homes”, Energies 2016, Vol. 9, No. 3, pp. 202, Mar. 2016. [39] A. Ahmad, N. Javaid, N. Alrajeh, Z. A. Khan, U. Qasim, and Abid Khan, “A Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid”, Applied Sciences 2015, Vol. 5, No. 4, pp. 1756-1772, Dec. 2016.

VIII. G LOSSARY A. Traditional Power Grid Traditional power grid is an interconnected complex organization for the distribution of electricity from energy utility companies to consumers. It has three basic components that are generation stations for electrical power production, complex infrastructure of high-voltage transmission lines that carry power from sources to demand area, and distribution stations that connect different area for electricity supply. B. Smart Grid A smart grid is an emerging idea to modify traditional electrical grid comprising of variety of automatic and digital energy measuring devices including smart meters, numerous operational and control units, vast communication infrastructure and high penetration of renewable energy resources and energy storage systems.

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C. DSM It is one of the most important aspects of SG that deals with load shifting, energy efficiency and energy conversation to reduce electricity bills of end users, avoid peak formation and maintain balance between demand and supply. D. Energy management system It is computerized system used by operators of electricity grids to control, monitor and optimize the capacity of generation and transmission units. E. Smart meter It is an electronic device used for bi-directional flow of information between meters and the central power grid system. F. DR It is a responsive feature of DSM that provides an opportunity for end users to play a significant role in home energy management system by changing their power consumption patterns during peak hours in response to dynamic tariff programs. G. Dynamic pricing tariff It is also called time-based pricing model. It is a pricing strategy, where energy utility companies offer flexible prices of electricity depending on the different time of a day to give benefit to their consumers. H. TOU pricing tariff In this tariff model, electricity prices are pre-defined for a specific time slot, typically remain fixed for a year or change twice in a year. As electricity prices for each slot are known to consumers, they can vary their usage in response to such prices and manage their electricity bills. I. RTP tariff model In this model, electricity prices are regularly changed in a specific time interval. Users are provided with price signal in advance to manage their electricity usage.

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J. IBR tariff model IBR is the division of electricity price into several levels or blocks of electrical power consumed. The first block is at the lowest price, second block is at higher price level and so on. The procedure to transfer from one block to next is automatic and depends only on the amount of electricity consumed by the end users. At the end of the month, the history is reset and the customer will again start from block 1 for the next month. K. Energy utility Company An energy utility company in the power industry is a company that does not own or operate power generation plants, transmission and distribution units but only buys and sells electricity. These utilities are regulated by local and national authorities. L. Scheduling horizon Total time period in which we want to schedule all smart appliances in the smart area. M. System Outage It refers to failure of electricity system to perform its primary task due to the shortage of electricity. N. RESs These resources are the elements of economic value that can be replaced in the same amount or less as used. Such as solar, water, geothermal pressure, etc. O. Prosumers Electricity users who can produce electricity through RESs while consuming electricity from power grid at the same time for electricity bill reduction. P. Knapsack problems It is a combinatorial optimization technique where set of different items, each one having a weight and a value include in collection such that total weight of the included items is less than or equal to the assigned capacity and total value is as large as possible.

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Q. Heuristic algorithms Algorithms that find best solution among all possible ones, but they do not guarantee accuracy that is why they are considered as approximate algorithms.

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